Data Engineering Project: European Soccer ⚽️⚽️⚽️

_{Amr M. Kayid}	_{Omar ElSayed}	_{Sama AlShareef}	_{Mayar Kamali}	_{Zeiad Helmy}
37-15594	37-6537	37-705	37-4473	37-14353
T10	T10	T10	T10	T10

Overview¶

What you get:

+25,000 matches +10,000 players 11 European Countries with their lead championship Seasons 2008 to 2016 Players and Teams' attributes sourced from EA Sports' FIFA video game series, including the weekly updates Team line up with squad formation (X, Y coordinates) Betting odds from up to 10 providers Detailed match events (goal types, possession, corner, cross, fouls, cards etc...) for +10,000 matches16th Oct 2016: New table containing teams' attributes from FIFA !

Original Data Source:

You can easily find data about soccer matches but they are usually scattered across different websites. A thorough data collection and processing has been done to make your life easier. I must insist that you do not make any commercial use of the data. The data was sourced from:

http://football-data.mx-api.enetscores.com/ : scores, lineup, team formation and events

http://www.football-data.co.uk/ : betting odds. Click here to understand the column naming system for betting odds:

http://sofifa.com/ : players and teams attributes from EA Sports FIFA games. FIFA series and all FIFA assets property of EA Sports.

When you have a look at the database, you will notice foreign keys for players and matches are the same as the original data sources. I have called those foreign keys "api_id".

Improving the dataset:

You will notice that some players are missing from the lineup (NULL values). This is because I have not been able to source their attributes from FIFA. This will be fixed overtime as the crawling algorithm is being improved. The dataset will also be expanded to include international games, national cups, Champion's League and Europa League. Please ask me if you're after a specific tournament.

Please get in touch with me if you want to help improve this dataset.

CLICK HERE TO ACCESS THE PROJECT GITHUB

Important note for people interested in using the crawlers: since I first wrote the crawling scripts (in python), it appears sofifa.com has changed its design and with it comes new requirements for the scripts. The existing script to crawl players ('Player Spider') will not work until i've updated it.

Exploring the data:

Now that's the fun part, there is a lot you can do with this dataset. I will be adding visuals and insights to this overview page but please have a look at the kernels and give it a try yourself ! Here are some ideas for you:

The Holy Grail... ... is obviously to predict the outcome of the game. The bookies use 3 classes (Home Win, Draw, Away Win). They get it right about 53% of the time. This is also what I've achieved so far using my own SVM. Though it may sound high for such a random sport game, you've got to know that the home team wins about 46% of the time. So the base case (constantly predicting Home Win) has indeed 46% precision.

Probabilities vs Odds

When running a multi-class classifier like SVM you could also output a probability estimate and compare it to the betting odds. Have a look at your variance vs odds and see for what games you had very different predictions.

Explore and visualize features

With access to players and teams attributes, team formations and in-game events you should be able to produce some interesting insights into The Beautiful Game . Who knows, Guardiola himself may hire one of you some day!

Libraries¶

%reload_ext autoreload
%autoreload 2
%matplotlib inline

# import os

# os.environ["MODIN_ENGINE"] = "ray"  # Modin will use Ray
# # os.environ["MODIN_ENGINE"] = "dask"  # Modin will use Dask

import io
import math
import base64
import folium
import sqlite3
import warnings
import itertools
import folium.plugins
import time, datetime

import scipy
# import psycopg2
import numpy as np
import pandas as pd
# import modin.pandas as pd
import seaborn as sns
import plotly.tools as tls
import plotly.offline as py
import plotly.graph_objs as go
import matplotlib.pyplot as plt

import scipy.ndimage

from scipy import stats
from collections import *
from matplotlib.pyplot import imread
from statsmodels.stats.power import TTestIndPower

from pathlib import Path
from datetime import timedelta
from subprocess import check_output
from matplotlib import animation, rc
from mpl_toolkits.basemap import Basemap

warnings.filterwarnings("ignore")
py.init_notebook_mode(connected=True)

Configs¶

PATH = Path(f'data/')

from IPython.core.display import HTML
from IPython.display import display

def display_tables(table_dict):
    ''' 
    Accepts a list of IpyTable objects and returns a table which contains each IpyTable in a cell
    ''' 
    template = """<div style="float: left; padding: 10px;">
                    <p style='font-family:"Courier New", Courier, monospace'>
                    <strong>{0}</strong></p>{1}</div>"""
    
    return HTML(
        '<table><tr style="background-color:white;">' + 
        '\n\n'.join(['<td>' + template.format(repr(key), table._repr_html_()) +
                     '</td>' for key, table in table_dict.items()]) +
        '</tr></table>'
    )

Data Loading and Parsing¶

class DataBunch:
    __dfs__ = ['countries', 'leagues', 'matches', 'players', 
               'player_attributes', 'teams', 'team_attributes', 'sqlite_sequences']
    
    @classmethod
    def connect(cls, path):
        connection = sqlite3.connect(str(path/'database.sqlite'))
        return connection
    
    def __init__(self, path):
        self.path = path
        self.connection = DataBunch.connect(path)
        self.tables = self.get_all_tables(self.connection)
        self.dfs = dict.fromkeys(DataBunch.__dfs__ , None)
        self.lat_long = pd.read_excel(path/'latlong.xlsx', sheet_name="Sheet1")
        self.set_tables(self.tables, self.connection)

    def get_all_tables(self, connection):
        cursor = connection.cursor()
        cursor.execute(f"SELECT name FROM sqlite_master WHERE type='table';")
        tables = cursor.fetchall()
        table_names = sorted([table[0] for table in tables])
        return table_names

    def set_tables(self, table_names, connection):
        dataframes = {}
        for i, name in enumerate(table_names):
            print(f'Processing {i}: {DataBunch.__dfs__[i]} dataframe | from {name} table')
            dataframes[DataBunch.__dfs__[i]] = pd.read_sql_query(
                f"SELECT * from {name}", connection)
        for key, value in dataframes.items():
            setattr(self, f'_{key}_df', value)
            self.dfs[key] = value
            
    def describe_and_check_nulls(self):
        
        cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)

        def magnify():
            return [dict(selector="th",
                         props=[("font-size", "7pt")]),
                    dict(selector="td",
                         props=[('padding', "0em 0em")]),
                    dict(selector="th:hover",
                         props=[("font-size", "12pt")]),
                    dict(selector="tr:hover td:hover",
                         props=[('max-width', '200px'),
                                ('font-size', '12pt')])
        ]

        dfs_with_nulls = {}
        for name, df in self.dfs.items():
            print('=' * 50 +  f' {name} ' + '=' * 50)
            print(f'{name} INFO:')
            display(df.info())
            print()
            print(f'{name} Describtion:')
            display(df.describe().transpose())
            print()
            print(f'{name} Correlations:')
            corr = df.corr()
            display(corr.style.background_gradient(cmap, axis=1)\
                    .set_properties(**{'max-width': '80px', 'font-size': '10pt'})\
                    .set_caption("Hover to magify")\
                    .set_precision(2)\
                    .set_table_styles(magnify()))
            print()
            print(f'{name} NULLs:')
            display(df.isnull().sum())
            if df.isnull().sum().any():
                print(f'Found df {name} with nulls.....')
                dfs_with_nulls[name] = df
            print('\n'*5)
        return dfs_with_nulls

db = DataBunch(PATH)

Processing 0: countries dataframe | from Country table
Processing 1: leagues dataframe | from League table
Processing 2: matches dataframe | from Match table
Processing 3: players dataframe | from Player table
Processing 4: player_attributes dataframe | from Player_Attributes table
Processing 5: teams dataframe | from Team table
Processing 6: team_attributes dataframe | from Team_Attributes table
Processing 7: sqlite_sequences dataframe | from sqlite_sequence table

db.__dict__.keys()

dict_keys(['path', 'connection', 'tables', 'dfs', 'lat_long', '_countries_df', '_leagues_df', '_matches_df', '_players_df', '_player_attributes_df', '_teams_df', '_team_attributes_df', '_sqlite_sequences_df'])

db.dfs.keys()

dict_keys(['countries', 'leagues', 'matches', 'players', 'player_attributes', 'teams', 'team_attributes', 'sqlite_sequences'])

Display All tables in the databse¶

display_tables(db.dfs)

Data Cleaning¶

dfs_with_nulls = db.describe_and_check_nulls()

================================================== countries ==================================================
countries INFO:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11 entries, 0 to 10
Data columns (total 2 columns):
id      11 non-null int64
name    11 non-null object
dtypes: int64(1), object(1)
memory usage: 304.0+ bytes

None

countries Describtion:

countries Correlations:

countries NULLs:

id      0
name    0
dtype: int64



================================================== leagues ==================================================
leagues INFO:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11 entries, 0 to 10
Data columns (total 3 columns):
id            11 non-null int64
country_id    11 non-null int64
name          11 non-null object
dtypes: int64(2), object(1)
memory usage: 392.0+ bytes

None

leagues Describtion:

leagues Correlations:

What are the tables that contains NULL values¶

dfs_with_nulls.keys()

dict_keys(['matches', 'player_attributes', 'teams', 'team_attributes'])

Matches Dataframe¶

df = db.dfs['matches'].copy()

df.head(3)

df['league_id'].nunique()

11

df.isnull().sum()

id                0
country_id        0
league_id         0
season            0
stage             0
              ...  
GBD           11817
GBA           11817
BSH           11818
BSD           11818
BSA           11818
Length: 115, dtype: int64

# How many total missing values do we have?
total_cells = np.product(df.shape) 
total_missing = df.isnull().sum().sum()

# percent of data that is missing
percentage = (total_missing / total_cells) * 100
print(percentage)

13.63626474225838

If we dropped null values, there will be missing teams in the dataframes!¶

df.dropna()['league_id'].nunique()

5

We will fill the missing values using the mean¶

df = df.fillna(df.mean())

db.dfs['matches'] = df

Player_Attributes Dataframe¶

df = db.dfs['player_attributes'].copy()

df.head(3)

df.isnull().sum()

id                        0
player_fifa_api_id        0
player_api_id             0
date                      0
overall_rating          836
potential               836
preferred_foot          836
attacking_work_rate    3230
defensive_work_rate     836
crossing                836
finishing               836
heading_accuracy        836
short_passing           836
volleys                2713
dribbling               836
curve                  2713
free_kick_accuracy      836
long_passing            836
ball_control            836
acceleration            836
sprint_speed            836
agility                2713
reactions               836
balance                2713
shot_power              836
jumping                2713
stamina                 836
strength                836
long_shots              836
aggression              836
interceptions           836
positioning             836
vision                 2713
penalties               836
marking                 836
standing_tackle         836
sliding_tackle         2713
gk_diving               836
gk_handling             836
gk_kicking              836
gk_positioning          836
gk_reflexes             836
dtype: int64

# How many total missing values do we have?
total_cells = np.product(df.shape) 
total_missing = df.isnull().sum().sum()

# percent of data that is missing
percentage = (total_missing / total_cells) * 100
print(percentage)

0.6121461727566805

If we dropped null values, there will be missing teams in the dataframes!¶

df.dropna()

len(df), len(df.dropna()), 'Difference =>', len(df) - len(df.dropna())

(183978, 180354, 'Difference =>', 3624)

We can drop the missing values but since there are multiple records for the same players, we will be use the filling method that fills the records with the values next to the record¶

df = df.fillna(method='bfill', axis=0).fillna(0)

db.dfs['player_attributes'] = df

Teams Dataframe¶

df = db.dfs['teams'].copy()

df.head(3)

df.isnull().sum()

id                   0
team_api_id          0
team_fifa_api_id    11
team_long_name       0
team_short_name      0
dtype: int64

# How many total missing values do we have?
total_cells = np.product(df.shape) 
total_missing = df.isnull().sum().sum()

# percent of data that is missing
percentage = (total_missing / total_cells) * 100
print(percentage)

0.7357859531772575

df[df.isnull().any(axis=1)]

We might not need the _team_fifa_apiid hence we are going to drop the column of this data¶

df = df.drop(columns=['team_fifa_api_id'])

db.dfs['teams'] = df

Team_Attributes Dataframe¶

df = db.dfs['team_attributes'].copy()

df.head(3)

df.isnull().sum()

id                                  0
team_fifa_api_id                    0
team_api_id                         0
date                                0
buildUpPlaySpeed                    0
buildUpPlaySpeedClass               0
buildUpPlayDribbling              969
buildUpPlayDribblingClass           0
buildUpPlayPassing                  0
buildUpPlayPassingClass             0
buildUpPlayPositioningClass         0
chanceCreationPassing               0
chanceCreationPassingClass          0
chanceCreationCrossing              0
chanceCreationCrossingClass         0
chanceCreationShooting              0
chanceCreationShootingClass         0
chanceCreationPositioningClass      0
defencePressure                     0
defencePressureClass                0
defenceAggression                   0
defenceAggressionClass              0
defenceTeamWidth                    0
defenceTeamWidthClass               0
defenceDefenderLineClass            0
dtype: int64

# How many total missing values do we have?
total_cells = np.product(df.shape) 
total_missing = df.isnull().sum().sum()

# percent of data that is missing
percentage = (total_missing / total_cells) * 100
print(percentage)

2.6584362139917697

df[df.isnull().any(axis=1)]

We might not need the buildUpPlayDribbling hence we are going to drop the column of this data¶

df = df.drop(columns=['buildUpPlayDribbling'])

db.dfs['team_attributes'] = df

Display Cleaned Data 🎉 🧼¶

display_tables(db.dfs)

Some Data Modification & Enhancements 🔝¶

Player Dataframe:¶

Removing unnessecary time from the birth day
Adding new Column {Age} to the player df

df = db.dfs['players'].copy()
df.head(3)

df['birthday'] =  pd.to_datetime(df['birthday'])
df.head(3)

now = pd.Timestamp('now')
df['age'] = (now - df['birthday']).astype('<m8[Y]').apply(int)
df.head(3)

db.dfs['players'] = df

Player_Attributes Dataframe¶

df = db.dfs['player_attributes'].copy()
df.head(3)

players_and_attr = (db.dfs['players']
                    .merge(df, on="player_api_id", how='outer')
                   .rename(columns={'player_fifa_api_id_x':"player_fifa_api_id"}))

players_and_attr = players_and_attr.drop(["id_x", "id_y", "player_fifa_api_id_y"], axis = 1)
df = db.dfs['players_and_attr'] = players_and_attr

df

We can observe that multiple record for the same player are stored over the years!¶

df.groupby('player_name').count()

df.sort_values("date", inplace=True, ascending=False)

df

df.drop_duplicates(subset ="player_name", 
                     keep = 'first', inplace = True)

df.groupby('player_name').count()

players_in_2016 = df[df['date'].str.contains('2016')]

db.dfs['players_in_2016'] = players_in_2016

Merging Counteries & Leagues Dataframes¶

df1, df2 = db.dfs['countries'].copy(), db.dfs['leagues'].copy()
df = db.dfs['leagues_by_countries'] = (df1.merge(df2, left_on="id", right_on="id", how="outer")
                                           .rename(columns={'name_x':"country", 'name_y':"league"}))
df = df.drop("id", axis = 1)
df

Matches Dataframe¶

New Dataframw matches_with_less_attr with less attributes from matches df
Merging with leagues df

df = db.dfs['matches'].copy()
df.head(3)

list(df.columns)

['id',
 'country_id',
 'league_id',
 'season',
 'stage',
 'date',
 'match_api_id',
 'home_team_api_id',
 'away_team_api_id',
 'home_team_goal',
 'away_team_goal',
 'home_player_X1',
 'home_player_X2',
 'home_player_X3',
 'home_player_X4',
 'home_player_X5',
 'home_player_X6',
 'home_player_X7',
 'home_player_X8',
 'home_player_X9',
 'home_player_X10',
 'home_player_X11',
 'away_player_X1',
 'away_player_X2',
 'away_player_X3',
 'away_player_X4',
 'away_player_X5',
 'away_player_X6',
 'away_player_X7',
 'away_player_X8',
 'away_player_X9',
 'away_player_X10',
 'away_player_X11',
 'home_player_Y1',
 'home_player_Y2',
 'home_player_Y3',
 'home_player_Y4',
 'home_player_Y5',
 'home_player_Y6',
 'home_player_Y7',
 'home_player_Y8',
 'home_player_Y9',
 'home_player_Y10',
 'home_player_Y11',
 'away_player_Y1',
 'away_player_Y2',
 'away_player_Y3',
 'away_player_Y4',
 'away_player_Y5',
 'away_player_Y6',
 'away_player_Y7',
 'away_player_Y8',
 'away_player_Y9',
 'away_player_Y10',
 'away_player_Y11',
 'home_player_1',
 'home_player_2',
 'home_player_3',
 'home_player_4',
 'home_player_5',
 'home_player_6',
 'home_player_7',
 'home_player_8',
 'home_player_9',
 'home_player_10',
 'home_player_11',
 'away_player_1',
 'away_player_2',
 'away_player_3',
 'away_player_4',
 'away_player_5',
 'away_player_6',
 'away_player_7',
 'away_player_8',
 'away_player_9',
 'away_player_10',
 'away_player_11',
 'goal',
 'shoton',
 'shotoff',
 'foulcommit',
 'card',
 'cross',
 'corner',
 'possession',
 'B365H',
 'B365D',
 'B365A',
 'BWH',
 'BWD',
 'BWA',
 'IWH',
 'IWD',
 'IWA',
 'LBH',
 'LBD',
 'LBA',
 'PSH',
 'PSD',
 'PSA',
 'WHH',
 'WHD',
 'WHA',
 'SJH',
 'SJD',
 'SJA',
 'VCH',
 'VCD',
 'VCA',
 'GBH',
 'GBD',
 'GBA',
 'BSH',
 'BSD',
 'BSA']

list(df.columns)[:11]

['id',
 'country_id',
 'league_id',
 'season',
 'stage',
 'date',
 'match_api_id',
 'home_team_api_id',
 'away_team_api_id',
 'home_team_goal',
 'away_team_goal']

db.dfs['matches_with_less_attr'] = df[list(df.columns)[:11]].drop("id",axis=1)
db.dfs['matches_with_less_attr'].head(3)

merge leauge data with match data¶

leagues_and_matches = db.dfs['matches_with_less_attr'].merge(db.dfs['leagues_by_countries'],
                                              left_on="country_id",
                                              right_on="country_id",
                                              how="outer")

db.dfs['leagues_and_matches'] = leagues_and_matches
leagues_and_matches.head(3)

Visualization¶

Matches in our Earth Map 🌍¶

#getting lat lon info
leagues_by_countries = db.dfs['leagues_by_countries']
lat_long = db.lat_long
country_info  = leagues_by_countries.merge(lat_long, left_on="country", right_on="name", how="left")
country_info  = country_info.drop(["country_id","country_y","name"],axis = 1)


m3 = Basemap(projection='ortho', resolution=None, lat_0=50, lon_0=10,urcrnrlat=80,llcrnrlat=-80)

plt.figure(figsize=(15, 15))

country = list(country_info["country_x"].unique())
c       = sns.color_palette("Set2",len(country))
label   = country

def function(country,c,label):
    lat = list(country_info[country_info["country_x"] == country].latitude)
    lon = list(country_info[country_info["country_x"] == country].longitude)
    x,y = m3(lon,lat)
    m3.plot(x,y,"go",markersize=15,color=j,alpha=.8,label=i)

for i,j in zip(country,c):
    function(i,j,i)

m3.bluemarble(scale=0.5)
plt.legend(loc="center right",frameon=True,prop={"size":15}).get_frame().set_facecolor("white")
plt.title("Countries & Matches")
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

db.dfs.keys()

dict_keys(['countries', 'leagues', 'matches', 'players', 'player_attributes', 'teams', 'team_attributes', 'sqlite_sequences', 'players_and_attr', 'players_in_2016', 'leagues_by_countries', 'matches_with_less_attr', 'leagues_and_matches'])

df = db.dfs['leagues_and_matches']

plt.figure(figsize=(8,8))
ax = sns.countplot(y = df["league"],
                   order=df["league"].value_counts().index,
                   linewidth = 1,
                   edgecolor = "k"*df["league"].nunique()
                 )
for i,j in enumerate(df["league"].value_counts().values):
    ax.text(.7,i,j,weight = "bold")
plt.title("Matches by league")
plt.show()

df.groupby("league").agg({"home_team_goal":"sum","away_team_goal":"sum"}).plot(kind="barh",
                                                                                 figsize = (10,10),
                                                                                 edgecolor = "k",
                                                                                 linewidth =1
                                                                                )
plt.title("Home and away goals by league")
plt.legend(loc = "best" , prop = {"size" : 14})
plt.xlabel("total goals")
plt.show()

Visualization of English Premier League Matchday Squads¶

db.dfs.keys()

dict_keys(['countries', 'leagues', 'matches', 'players', 'player_attributes', 'teams', 'team_attributes', 'sqlite_sequences', 'players_and_attr', 'players_in_2016', 'leagues_by_countries', 'matches_with_less_attr', 'leagues_and_matches'])

def viz_matchday_squads(matches_df=db.dfs['matches'], players_df = db.dfs["players"], 
                        team_df = db.dfs['teams'], league_id = 1729, match_api_id = 489051):
    r"""
    This method is used to get all the data needed to visualize the arena, teams players and strategies of each team
    """
    
    match_details = matches_df[matches_df['match_api_id'] == match_api_id]
#     print(match_details)
    
    # Position of the GK is set to (1,1) in the dataset which is incorrect
    match_details['home_player_X1'] = 5
    match_details['home_player_Y1'] = 0
    match_details['away_player_X1'] = 5
    match_details['away_player_Y1'] = 0
    
    
    home_x_coordinates, home_y_coordinates = [], []
    away_x_coordinates, away_y_coordinates = [], []
    home_players, away_players = [], []
    
    # Obtain the coordinates to denote each player's position on the field
    for i in range(1, 12):
        home_player_x_coordinate = 'home_player_X%d' % i
        home_player_y_coordinate = 'home_player_Y%d' % i
        away_player_x_coordinate = 'away_player_X%d' % i
        away_player_y_coordinate = 'away_player_Y%d' % i

        home_x_coordinates.append(match_details[home_player_x_coordinate].iloc[0])
        home_y_coordinates.append((match_details[home_player_y_coordinate].iloc[0] + 15))
        away_x_coordinates.append(match_details[away_player_x_coordinate].iloc[0])
        away_y_coordinates.append((match_details[away_player_y_coordinate].iloc[0] + 35))

        # Obtain the players' names
        home_players.append(list(players_df[players_df['player_api_id'] 
                                            == match_details['home_player_%d' % i].iloc[0]]['player_name'])[0])

        away_players.append(list(players_df[players_df['player_api_id']
                                            == match_details['away_player_%d' % i].iloc[0]]['player_name'])[0])
        
        
    # Names of the Teams
    home_team = team_df[team_df['team_api_id'] == match_details['home_team_api_id'].iloc[0]]['team_long_name'].iloc[0]
    away_team = team_df[team_df['team_api_id'] == match_details['away_team_api_id'].iloc[0]]['team_long_name'].iloc[0]
    home_team, away_team
    
    
    #Formations of the Teams
    home_formation = np.unique(home_y_coordinates, return_counts = True)[1]
    away_formation = np.unique(away_y_coordinates, return_counts = True)[1]    

    img_path = PATH/'arena.jpg'
    
    
    # Home team in Orange
    plt.figure(figsize=(20,13))
    for label, x, y in zip(home_players, home_x_coordinates, home_y_coordinates):
        plt.annotate(
            label,
            xy = (x, y), xytext = (len(label)*-4, 20),
            textcoords = 'offset points',
            fontsize= 15,
            color = '#F2F3F4'
        )
    img = imread(img_path) #Background field image
    plt.title(home_team, loc = 'left', fontsize = 25)
    plt.title("Home Team", fontsize = 25)

    formation = "Formation: "
    for i in range(1,len(home_formation)):
        formation = formation + str(home_formation[i]) + "-"
    formation = formation[:-1]

    plt.title(formation, loc = 'right', fontsize = 25)
    plt.scatter(home_x_coordinates, home_y_coordinates, s = 500, color = '#F57C00', zorder = 2)
    plt.imshow(scipy.ndimage.rotate(img, 270), zorder = 1, extent=[min(home_x_coordinates)-1, max(home_x_coordinates)+1, min(home_y_coordinates)-1, max(home_y_coordinates)+1.7], aspect = 'auto')
    plt.gca().invert_yaxis() # Invert y axis to start with the goalkeeper at the top


    # Away team in Blue
    plt.figure(figsize=(20, 13))
    plt.gca().invert_xaxis() # Invert x axis to have right wingers on the right
    for label, x, y in zip(away_players, away_x_coordinates, away_y_coordinates):
        plt.annotate(
            label,
            xy = (x, y), xytext = (len(label)*-4, -30),
            textcoords = 'offset points',
            fontsize= 15,
            color = '#F2F3F4'
        )
    img = imread(img_path)
    plt.title(away_team, loc = 'left', fontsize = 25)
    plt.title("Away Team", fontsize = 25)

    formation = "Formation: "
    for i in range(1,len(away_formation)):
        formation = formation + str(away_formation[i]) + "-"
    formation = formation[:-1]

    plt.title(formation, loc = 'right', fontsize = 25)
    plt.scatter(away_x_coordinates, away_y_coordinates, s = 500, color = '#0277BD', zorder = 2)
    plt.imshow(scipy.ndimage.rotate(img, 270), zorder = 1, extent=[min(away_x_coordinates)-1, max(away_x_coordinates)+1, min(away_y_coordinates)-1, max(away_y_coordinates)+1.6], aspect = 'auto')
    plt.show()

viz_matchday_squads()

viz_matchday_squads(league_id=24558, match_api_id=1992095)

Questions & Hypothesis¶

Is there a significant difference in win rate for home teams vs. away teams?¶

Is there a statistical difference in the odds of winning a game when a team is playing in front of their home crowd?¶ H0 (Null Hypothesis): mean_win_rate_home = mean_win_rate_away there is no statistically significant difference in the odds of winning a game when a team is at playing at home vs. when a team is playing away HA (Alternative Hypothesis): mean_win_rate_home != mean_win_rate_away

there is a statistically significant difference in the odds of winning a game when a team is playing at home vs. when a team is playing away Alpha = 0.05

Assumptions for a 2-sample T-test: Data is collected randomly Data is independent Data is approximately normally distributed

db.dfs.keys()

dict_keys(['countries', 'leagues', 'matches', 'players', 'player_attributes', 'teams', 'team_attributes', 'sqlite_sequences', 'players_and_attr', 'players_in_2016', 'leagues_by_countries', 'matches_with_less_attr', 'leagues_and_matches'])

create new columns home_team_win & away_team win with binary values as such:¶

Home team winning = 1,
Tie=0,
Away team winning = 0

matches_df = db.dfs['matches']

matches_df['home_team_win'] = np.zeros
matches_df['away_team_win'] = np.zeros

# Home Team Values

#WIN
matches_df['home_team_win'].loc[matches_df['home_team_goal'] > matches_df['away_team_goal']] = 1
#LOSS
matches_df['home_team_win'].loc[matches_df['home_team_goal'] < matches_df['away_team_goal']] = 0
#TIE
matches_df['home_team_win'].loc[matches_df['home_team_goal'] == matches_df['away_team_goal']] = 0

# Away Team Values

#WIN
matches_df['away_team_win'].loc[matches_df['home_team_goal'] < matches_df['away_team_goal']] = 1
#LOSS
matches_df['away_team_win'].loc[matches_df['home_team_goal'] > matches_df['away_team_goal']] = 0
#TIE
matches_df['away_team_win'].loc[matches_df['home_team_goal'] == matches_df['away_team_goal']] = 0

#create numpy arrays
home_team_win_array = np.array(matches_df['home_team_win'])
away_team_win_array = np.array(matches_df['away_team_win'])

#the means of each array represent the win rate: win rate = matches won / matches NOT won (tie or loss)
x_bar_home = np.mean(home_team_win_array)
x_bar_away = np.mean(away_team_win_array)

#calculate the difference between the means, using all rows in the dataset
diff = x_bar_home - x_bar_away
diff

0.17133069017283187

len(home_team_win_array), len(away_team_win_array)

(25979, 25979)

Rate of Winning & Difference¶

n_home = len(home_team_win_array)

n_away = len(away_team_win_array)

home_wins = sum(home_team_win_array)
away_wins = sum(away_team_win_array)

home_win_rate = home_wins/n_home
away_win_rate = away_wins/n_home

diff = home_win_rate-away_win_rate
print(f"Home Win Rate: {home_win_rate} \nAway Win Rate: {away_win_rate} \nDifference: {diff}")

Home Win Rate: 0.45871665576042187 
Away Win Rate: 0.28738596558759 
Difference: 0.17133069017283187

var_home = home_team_win_array.var()
var_away = away_team_win_array.var()
var_home, var_away

(0.24829568548839653, 0.20479527237087852)

# TODO: Cohen's d: Effect Size
pooled_var = (n_home * var_home + n_away * var_away) / (n_home + n_away)
cohens_d = (diff) / np.sqrt(pooled_var)
cohens_d

0.35996267005447524

Finding Ideal Sample Size given Effect Size, desired Power, and desired Alpha¶

# Initialize parameters
effect = cohens_d
alpha = 0.05
power = 0.95

# sample 2 / sample 1   
ratio = len(away_team_win_array) / len(home_team_win_array)

# Perform power analysis
analysis = TTestIndPower()
result = analysis.solve_power(effect, power=power, nobs1=None,ratio=ratio, alpha=alpha)
print(f"The minimum sample size: {result}")
print(f"Number of matches played: {len(away_team_win_array)}")

The minimum sample size: 201.5427376165529
Number of matches played: 25979

Using Bootstrapping to sample from the ~25K matches, specifying # of iterations & sample size of each iteration¶

sample_means_home = []
for _ in range(1000):
    sample_mean = np.random.choice(home_team_win_array, size=202).mean()
    sample_means_home.append(sample_mean)

sample_means_away = []
for _ in range(1000):
    sample_mean = np.random.choice(away_team_win_array, size=202).mean()
    sample_means_away.append(sample_mean)
len(sample_means_home), len(sample_means_away)

(1000, 1000)

sample_means_home[:10], sample_means_away[:10]

([0.4504950495049505,
  0.4405940594059406,
  0.3910891089108911,
  0.4900990099009901,
  0.504950495049505,
  0.47029702970297027,
  0.45544554455445546,
  0.43564356435643564,
  0.43564356435643564,
  0.4752475247524752],
 [0.297029702970297,
  0.2524752475247525,
  0.297029702970297,
  0.2722772277227723,
  0.2871287128712871,
  0.3217821782178218,
  0.30198019801980197,
  0.297029702970297,
  0.297029702970297,
  0.26732673267326734])

functions to calculate individual sample variances, pooled sample variance, and our t-statistic in a 2-sample T-test¶

def calc_variance(sample):
    '''Computes the variance a list of values'''
    sample_mean = np.mean(sample)
    return sum([(i - sample_mean)**2 for i in sample])

def calc_sample_variance(sample1, sample2):
    '''Computes the pooled variance 2 lists of values, using the calc_variance function'''
    n_1, n_2 = len(sample1), len(sample2)
    var1, var2 = calc_variance(sample1), calc_variance(sample2)
    return (var1 + var2) / ((n_1 + n_2) - 2)

def calc_twosample_tstatistic(expr, ctrl):
    '''Computes the 2-sample T-stat of 2 lists of values, using the calc_sample_variance function'''
    expr_mean, ctrl_mean = np.mean(expr), np.mean(ctrl)
    n_e, n_c = len(expr), len(ctrl)
    samp_var = calc_sample_variance(expr,ctrl)
    t = (expr_mean - ctrl_mean) / np.sqrt(samp_var * ((1/n_e)+(1/n_c)))
    return t

t_stat = calc_twosample_tstatistic(sample_means_home, sample_means_away)

t_stat

113.3472736492802

stats.ttest_ind(sample_means_home, sample_means_away)

Ttest_indResult(statistic=113.34727364928037, pvalue=0.0)

Plotting distributions of sample_means_home (Blue) vs. sample_means_away (Orange)¶

sns.set(color_codes=True)
sns.set(rc={'figure.figsize':(12,10)})
plt.title('Bootstrapped Win Rate Frequencies', fontsize='25')
plt.xlabel('Win Rate', fontsize='20')
plt.ylabel('Win Rate Frequency', fontsize='20')
sns.distplot(sample_means_home, label='Home Win Rates') # Blue distribution
sns.distplot(sample_means_away, label='Away Win Rates') # Orange distribution
plt.legend()
plt.show()

Visualizing the T-statistic & negative T-statistic in a probability density function graph¶

def visualize_t(t_stat, n_control, n_experimental):
    # initialize a matplotlib "figure"
    fig = plt.figure(figsize=(8,5))
    ax = fig.gca()
    # generate points on the x axis between -20 and 20:
    xs = np.linspace(-20, 20, 500)

    # use stats.t.pdf to get values on the probability density function for the t-distribution
    ys= stats.t.pdf(xs, (n_control+n_experimental-2), 0, 1)
    ax.plot(xs, ys, linewidth=3, color='darkred')

    ax.axvline(t_stat, color='black', linestyle='--', lw=5)
    ax.axvline(-t_stat, color='black', linestyle='--', lw=5)
    plt.xlabel('t-stat', fontsize='20')
    plt.ylabel('probability density', fontsize='20')
    plt.title('Probability Density of t-test',fontsize='25')

    plt.show()
    return None

n_home = len(home_team_win_array)
n_away = len(away_team_win_array)
visualize_t(t_stat, n_home, n_away)

## Calculate p_value manually
# Lower tail comulative density function returns area under the lower tail curve
df = len(sample_means_home)+len(sample_means_home)-2

tail = stats.t.cdf(-t_stat, df, 0, 1)

p_value = tail*2
print(p_value)

0.0

#DOUBLE CHECK WITH SCIPY
stats.t.sf(abs(t_stat), len(sample_means_home)+len(sample_means_away)-2)*2

0.0

#TRIPLE CHECK WITH SCIPY
stats.ttest_ind(sample_means_home, sample_means_away)

Ttest_indResult(statistic=113.34727364928037, pvalue=0.0)

Visualizing Home-Field Advantage¶

result = pd.merge(matches_df,
                  db.dfs['teams'][['team_long_name','team_api_id']],
                  left_on='home_team_api_id',
                  right_on='team_api_id',
                  how='left')
result.rename(columns={"team_long_name": "home_team_name"}, inplace=True)

result = result.drop(columns='team_api_id')

results = pd.merge(result,
                  db.dfs['teams'][['team_long_name','team_api_id']],
                  left_on='away_team_api_id',
                  right_on='team_api_id',
                  how='left')

results.rename(columns={"team_long_name": "away_team_name"}, inplace=True)

results = results.drop(columns='team_api_id')

results['winning_team'] = np.nan
results.head()

results['winning_team'].loc[results['home_team_goal'] > results['away_team_goal']] = results['home_team_name']
results['winning_team'].loc[results['home_team_goal'] < results['away_team_goal']] = results['away_team_name']

results.head()

home_team_win_df = results.groupby("home_team_name").agg({
        "home_team_win": "mean",
    })

home_team_win_df.sort_values(by= 'home_team_win',ascending=False)

away_team_win_df = results.groupby("away_team_name").agg({
        "away_team_win": "mean",
    })

away_team_win_df.sort_values(by= 'away_team_win',ascending=False)

plt.figure(figsize=(16,8)) 
plt.plot(home_team_win_df,away_team_win_df,'o', alpha = 0.4)
plt.plot([0,1],[0,1])
plt.xlabel('Home Win Rate',fontsize='20')
plt.ylabel('Away Win Rate',fontsize='20')
plt.title('Home Win Rate vs Away Win Rate',fontsize='20')
plt.xlim([0,1])
plt.ylim([0,1])

(0, 1)

Conclusion: There is a significant difference in win rate for home teams vs. away teams. Home-field advantage definitely exists!¶

db.dfs.keys()

dict_keys(['countries', 'leagues', 'matches', 'players', 'player_attributes', 'teams', 'team_attributes', 'sqlite_sequences', 'players_and_attr', 'players_in_2016', 'leagues_by_countries', 'matches_with_less_attr', 'leagues_and_matches'])

df = db.dfs['players_and_attr']
df

def show_player_stats(name='Lionel Messi'):
    # Players: 'Cristiano Ronaldo', 'Lionel Messi', 'Neymar', 'Heung-Min Son'...
    player_info = db.dfs['players_and_attr']
    player = player_info[player_info["player_name"] == name]
    cols = ['player_name','overall_rating', 'finishing', 
            'heading_accuracy', 'short_passing', 'dribbling', 
            'sprint_speed', 'shot_power', 'jumping', 'stamina',
            'strength', 'positioning', 'penalties', 'sliding_tackle']

    player = player[cols]
    player = player.groupby("player_name")[cols].mean().reset_index()

    plt.figure(figsize=(8,8))
    ax = plt.subplot(projection="polar")
    cats = list(player)[1:]
    N    = len(cats)

    mean_values = player_info.iloc[:,:].mean()
    mean_values = mean_values[cols]
    
    values = mean_values.drop("player_name").values.flatten().tolist()
    values += values[:1]
    angles = [n / float(N)*2* math.pi for n in range(N)]
    angles += angles[:1]

    plt.xticks(angles[:-1],cats,color="r",size=7)
    plt.ylim([0,100])
    plt.plot(angles,values,color='r',linewidth=2,linestyle="solid")
    plt.fill(angles,values,color='r',alpha=1)

    values = player.loc[0].drop("player_name").values.flatten().tolist()
    values += values[:1]
    angles = [n / float(N)*2* math.pi for n in range(N)]
    angles += angles[:1]

    plt.xticks(angles[:-1],cats,color="k",size=12)
    plt.ylim([0,100])
    plt.plot(angles,values,color='y',linewidth=3,linestyle="solid")
    plt.fill(angles,values,color='y',alpha=0.5)

    plt.gca().legend(('Average', name), bbox_to_anchor=(1, 0.5, 0.5, 0.5), loc=8)
    plt.title(name,color="b", fontsize=18)
    plt.subplots_adjust(wspace=.4,hspace=.4)

show_player_stats()

show_player_stats('Cristiano Ronaldo')

Which features have the highest correlation with overall_rating?¶

df = db.dfs['player_attributes']

df['overall_rating'].corr(df['penalties'])

0.3926222088639533

potential_features = ['acceleration', 'curve', 'free_kick_accuracy', 'ball_control', 'shot_power', 'stamina']

# check how the features are correlated with the overall ratings

for f in potential_features:
    related = df['overall_rating'].corr(df[f])
    print(f"{f}: {related}")

acceleration: 0.2452063826505644
curve: 0.3526143480590304
free_kick_accuracy: 0.3494238194241043
ball_control: 0.4439779241084229
shot_power: 0.4276109369596505
stamina: 0.3269068736524547

cols = ['potential',  'crossing', 'finishing', 'heading_accuracy',
       'short_passing', 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',
       'long_passing', 'ball_control', 'acceleration', 'sprint_speed',
       'agility', 'reactions', 'balance', 'shot_power', 'jumping', 'stamina',
       'strength', 'long_shots', 'aggression', 'interceptions', 'positioning',
       'vision', 'penalties', 'marking', 'standing_tackle', 'sliding_tackle',
       'gk_diving', 'gk_handling', 'gk_kicking', 'gk_positioning',
       'gk_reflexes']

# create a list containing Pearson's correlation between 'overall_rating' with each column in cols
correlations = [ df['overall_rating'].corr(df[f]) for f in cols ]

len(cols), len(correlations)

(34, 34)

# create a function for plotting a dataframe with string columns and numeric values

def plot_dataframe(df, y_label):  
    color='coral'
    fig = plt.gcf()
    fig.set_size_inches(20, 12)
    plt.ylabel(y_label)

    ax = df.correlation.plot(linewidth=3.3, color=color)
    ax.set_xticks(df.index)
    ax.set_xticklabels(df.attributes, rotation=75); #Notice the ; (remove it and see what happens !)
    plt.show()

# create a dataframe using cols and correlations

df2 = pd.DataFrame({'attributes': cols, 'correlation': correlations})

# let's plot above dataframe using the function we created
    
plot_dataframe(df2, 'Player\'s Overall Rating')

# create a dataset containing only the birthday, date and overall_ratings from the Field Players dataset.
df = db.dfs['players_and_attr'][['birthday','date','overall_rating']]
df.head(2)

# converting the birthday and date columns to date type
df['birthday'] = pd.to_datetime(df['birthday'])
df['date'] = pd.to_datetime(df['date'])

# adding a column listing the age for each row
df['age'] = df['date'].dt.year - df['birthday'].dt.year
df.head()

# finding the average ratings for each age group in the dataset
df_age_ratings = df.groupby('age').mean()

# finding the number of players in each group
df.groupby('age').count()

# setting up the parameters and plotting the scatter plot

locations = df_age_ratings.index.values
height = df_age_ratings['overall_rating']
plt.style.use('ggplot')

# plotting scatter plot
plt.plot(locations, height,'-o')

# set title and labels
plt.title('Age Vs Overall Rating Relationship Chart')
plt.xlabel('Age of the players')
plt.ylabel('Overall Ratings')
plt.rcParams['figure.figsize'] = (8,6)

	count	mean	std	min	25%	50%	75%	max
id	11.0	12452.090909	8215.308472	1.0	6289.0	13274.0	18668.0	24558.0
country_id	11.0	12452.090909	8215.308472	1.0	6289.0	13274.0	18668.0	24558.0

	count	mean	std	min	25%	50%	75%	max
id	25979.0	1.299000e+04	7499.635658	1.00	6495.50	12990.0	19484.50	25979.0
country_id	25979.0	1.173863e+04	7553.936759	1.00	4769.00	10257.0	17642.00	24558.0
league_id	25979.0	1.173863e+04	7553.936759	1.00	4769.00	10257.0	17642.00	24558.0
stage	25979.0	1.824277e+01	10.407354	1.00	9.00	18.0	27.00	38.0
match_api_id	25979.0	1.195429e+06	494627.856527	483129.00	768436.50	1147511.0	1709852.50	2216672.0
...	...	...	...	...	...	...	...	...
GBD	14162.0	3.648189e+00	0.867440	1.45	3.20	3.3	3.75	11.0
GBA	14162.0	4.353097e+00	3.010189	1.12	2.50	3.4	5.00	34.0
BSH	14161.0	2.497894e+00	1.507793	1.04	1.67	2.1	2.62	17.0
BSD	14161.0	3.660742e+00	0.868272	1.33	3.25	3.4	3.75	13.0
BSA	14161.0	4.405663e+00	3.189814	1.12	2.50	3.4	5.00	34.0

	id	country_id	league_id	stage	match_api_id	home_team_api_id	away_team_api_id	home_team_goal	away_team_goal	home_player_X1	home_player_X2	home_player_X3	home_player_X4	home_player_X5	home_player_X6	home_player_X7	home_player_X8	home_player_X9	home_player_X10	home_player_X11	away_player_X1	away_player_X2	away_player_X3	away_player_X4	away_player_X5	away_player_X6	away_player_X7	away_player_X8	away_player_X9	away_player_X10	away_player_X11	home_player_Y1	home_player_Y2	home_player_Y3	home_player_Y4	home_player_Y5	home_player_Y6	home_player_Y7	home_player_Y8	home_player_Y9	home_player_Y10	home_player_Y11	away_player_Y1	away_player_Y2	away_player_Y3	away_player_Y4	away_player_Y5	away_player_Y6	away_player_Y7	away_player_Y8	away_player_Y9	away_player_Y10	away_player_Y11	home_player_1	home_player_2	home_player_3	home_player_4	home_player_5	home_player_6	home_player_7	home_player_8	home_player_9	home_player_10	home_player_11	away_player_1	away_player_2	away_player_3	away_player_4	away_player_5	away_player_6	away_player_7	away_player_8	away_player_9	away_player_10	away_player_11	B365H	B365D	B365A	BWH	BWD	BWA	IWH	IWD	IWA	LBH	LBD	LBA	PSH	PSD	PSA	WHH	WHD	WHA	SJH	SJD	SJA	VCH	VCD	VCA	GBH	GBD	GBA	BSH	BSD	BSA
id	1	0.99	0.99	0.004	0.13	-0.016	-0.016	0.0086	0.011	-0.0034	0.0066	0.027	-0.017	0.046	-0.025	0.082	-0.06	-0.054	0.055	-0.023	0.0061	0.0055	0.015	-0.02	0.045	-0.014	0.086	-0.055	-0.056	0.038	-0.014	-0.0032	-0.0034	-0.0034	-0.01	-0.049	0.069	-0.014	0.1	0.033	-0.054	0.018	0.0071	nan	0.0048	0.002	-0.044	0.06	-0.017	0.096	0.035	-0.037	0.0076	0.11	0.14	0.16	0.17	0.11	0.15	0.15	0.13	0.12	0.14	0.11	0.1	0.15	0.16	0.16	0.11	0.14	0.14	0.12	0.12	0.14	0.11	0.064	0.1	0.036	0.061	0.11	0.041	0.063	0.096	0.041	0.07	0.12	0.045	0.079	0.12	0.052	0.066	0.11	0.044	0.056	0.098	0.033	0.075	0.12	0.056	0.051	0.095	0.025	0.049	0.096	0.024
country_id	0.99	1	1	-0.0022	0.039	-0.021	-0.021	0.0081	0.0089	-0.007	-0.0021	0.019	-0.018	0.054	-0.05	0.057	-0.048	-0.027	0.038	-0.016	0.0063	-0.0025	0.0067	-0.021	0.054	-0.04	0.063	-0.045	-0.029	0.024	-0.0098	-0.0066	-0.007	-0.0031	-0.01	-0.056	0.076	-0.0058	0.08	0.0091	-0.041	-5.8e-05	0.0072	nan	0.0051	0.002	-0.053	0.068	-0.0097	0.074	0.01	-0.026	-0.0086	0.073	0.1	0.13	0.13	0.073	0.11	0.11	0.091	0.076	0.1	0.074	0.07	0.1	0.13	0.13	0.068	0.11	0.11	0.085	0.076	0.1	0.077	0.06	0.093	0.034	0.057	0.096	0.038	0.058	0.082	0.039	0.064	0.1	0.038	0.079	0.12	0.049	0.06	0.1	0.039	0.054	0.09	0.032	0.069	0.11	0.05	0.05	0.088	0.023	0.048	0.089	0.022
league_id	0.99	1	1	-0.0022	0.039	-0.021	-0.021	0.0081	0.0089	-0.007	-0.0021	0.019	-0.018	0.054	-0.05	0.057	-0.048	-0.027	0.038	-0.016	0.0063	-0.0025	0.0067	-0.021	0.054	-0.04	0.063	-0.045	-0.029	0.024	-0.0098	-0.0066	-0.007	-0.0031	-0.01	-0.056	0.076	-0.0058	0.08	0.0091	-0.041	-5.8e-05	0.0072	nan	0.0051	0.002	-0.053	0.068	-0.0097	0.074	0.01	-0.026	-0.0086	0.073	0.1	0.13	0.13	0.073	0.11	0.11	0.091	0.076	0.1	0.074	0.07	0.1	0.13	0.13	0.068	0.11	0.11	0.085	0.076	0.1	0.077	0.06	0.093	0.034	0.057	0.096	0.038	0.058	0.082	0.039	0.064	0.1	0.038	0.079	0.12	0.049	0.06	0.1	0.039	0.054	0.09	0.032	0.069	0.11	0.05	0.05	0.088	0.023	0.048	0.089	0.022
stage	0.004	-0.0022	-0.0022	1	0.014	-0.0066	-0.0038	0.015	0.005	0.03	0.02	0.0014	-0.016	-0.0087	-0.016	-0.0056	0.0018	0.028	-0.0083	-0.0076	0.0057	0.018	0.0085	-0.0074	-0.023	-0.013	-0.0085	0.0077	0.034	-0.015	-0.003	0.025	0.034	-0.00034	0.00034	0.019	0.015	0.0015	-0.0021	-0.03	0.0016	-0.00021	0.0037	nan	0.0058	0.0067	0.028	0.0096	0.003	-0.0096	-0.028	0.0056	-0.0037	-0.0045	0.0076	0.016	-0.0068	0.0084	0.013	-0.0034	0.0096	-2.1e-05	0.015	-0.0039	0.0026	0.012	0.012	0.0017	0.011	0.013	0.0064	0.017	0.0048	0.011	0.0049	0.0072	0.031	0.0098	0.007	0.04	0.012	0.012	0.05	0.018	0.0073	0.038	0.014	0.0092	0.031	0.0038	0.013	0.032	0.016	0.005	0.031	0.012	0.0097	0.036	0.017	0.0059	0.033	0.012	0.0095	0.034	0.013
match_api_id	0.13	0.039	0.039	0.014	1	0.091	0.091	0.004	0.026	0.03	0.091	0.089	-0.0071	-0.067	0.21	0.21	-0.097	-0.25	0.17	-0.05	-0.0031	0.089	0.087	-0.0047	-0.074	0.23	0.2	-0.083	-0.25	0.13	-0.028	0.027	0.031	-0.0026	-0.0019	0.059	-0.016	-0.03	0.22	0.23	-0.12	0.16	-0.0037	nan	-0.0025	-0.0008	0.065	-0.028	-0.019	0.21	0.24	-0.094	0.14	0.39	0.49	0.41	0.42	0.46	0.47	0.44	0.48	0.49	0.47	0.44	0.39	0.49	0.41	0.43	0.46	0.47	0.44	0.48	0.49	0.47	0.45	0.046	0.11	0.0084	0.051	0.099	0.014	0.057	0.12	0.012	0.072	0.15	0.054	0.027	0.028	0.014	0.063	0.076	0.029	0.029	0.088	-0.0076	0.068	0.15	0.045	0.025	0.11	0.016	0.027	0.11	0.013
home_team_api_id	-0.016	-0.021	-0.021	-0.0066	0.091	1	0.0057	-0.016	0.016	0.0013	0.012	0.027	0.00012	-0.0073	0.0052	0.016	-0.0011	-0.026	0.017	0.0028	-0.00034	-0.00056	0.0057	-0.011	-0.0028	0.021	0.024	-0.00011	-0.029	0.003	0.0088	0.00097	0.0016	-0.0008	-0.0011	-0.00091	0.014	0.0013	0.0062	0.029	-0.0072	0.0098	-0.0006	nan	-0.00018	-0.00023	0.0037	-0.0029	-0.0084	0.011	0.032	0.0041	-0.0063	0.043	0.1	0.066	0.082	0.055	0.079	0.069	0.07	0.095	0.077	0.076	0.048	0.062	0.049	0.069	0.058	0.055	0.054	0.045	0.052	0.063	0.06	0.045	-0.022	-0.036	0.046	-0.02	-0.036	0.041	-0.018	-0.038	0.048	-0.022	-0.034	0.052	-0.031	-0.044	0.041	-0.021	-0.035	0.024	-0.015	-0.02	0.047	-0.019	-0.033	0.018	-0.0087	-0.011	0.018	-0.0067	-0.011
away_team_api_id	-0.016	-0.021	-0.021	-0.0038	0.091	0.0057	1	0.0069	-0.014	0.00099	0.00061	0.022	0.0043	-0.0083	0.01	0.012	0.0074	-0.025	0.0046	0.012	-0.0028	0.013	0.018	-0.0031	-0.012	0.0093	0.011	0.012	-0.016	-0.012	0.02	0.00085	0.0011	-0.00078	-0.0009	0.0078	0.01	0.0064	0.0029	0.025	0.0015	-0.0043	-0.002	nan	-0.0027	-0.0025	0.0065	0.012	0.0086	0.0041	0.027	0.016	-0.014	0.051	0.077	0.053	0.078	0.053	0.058	0.063	0.065	0.058	0.07	0.054	0.047	0.095	0.072	0.083	0.059	0.067	0.078	0.067	0.087	0.083	0.09	-0.032	0.018	0.039	-0.033	0.018	0.041	-0.033	0.023	0.036	-0.031	0.016	0.046	-0.04	0.013	0.051	-0.032	0.019	0.033	-0.017	0.0075	0.016	-0.03	0.019	0.037	-0.012	0.0087	0.026	-0.012	0.011	0.021
home_team_goal	0.0086	0.0081	0.0081	0.015	0.004	-0.016	0.0069	1	-0.064	0.0022	0.0067	-0.0053	-0.0066	0.014	-0.0056	0.028	0.019	-0.035	-0.029	0.05	-0.0065	-0.018	-0.014	-0.016	0.0073	0.021	-0.0047	-0.016	-0.00066	0.015	-0.011	0.0013	0.0031	-0.0027	0.00051	-0.0075	0.023	0.00088	-0.00046	0.049	0.043	-0.037	-0.0019	nan	-0.0077	-0.0081	-0.0095	-0.019	0.0012	0.017	-0.0044	-0.014	0.013	-0.016	-0.027	-0.0039	-0.011	-0.025	-0.01	-0.0065	-0.017	-0.013	-0.034	-0.034	0.035	0.028	0.027	0.017	0.023	0.016	0.025	0.011	0.013	0.026	0.024	-0.26	0.27	0.36	-0.26	0.27	0.35	-0.27	0.27	0.36	-0.26	0.26	0.35	-0.25	0.27	0.35	-0.26	0.27	0.35	-0.25	0.27	0.35	-0.25	0.27	0.34	-0.26	0.27	0.35	-0.25	0.27	0.35
away_team_goal	0.011	0.0089	0.0089	0.005	0.026	0.016	-0.014	-0.064	1	-0.013	-0.022	-0.0075	-0.0069	0.01	0.0095	-0.01	-0.011	0.0039	0.012	-0.014	0.015	0.018	-0.0018	-0.009	0.01	-0.0028	0.028	0.023	-0.034	-0.037	0.049	-0.011	-0.015	-0.0066	-0.0047	-0.0083	-0.007	0.0091	0.011	-0.0087	-0.014	0.012	0.0081	nan	0.016	0.016	-0.0057	0.023	0.0019	-0.0062	0.045	0.048	-0.04	0.028	0.029	0.028	0.025	0.036	0.037	0.018	0.022	0.025	0.026	0.031	-0.013	-0.0067	-0.007	-0.0065	-0.011	0.0064	0.0047	-0.00041	-0.0039	-0.022	-0.0074	0.3	-0.074	-0.23	0.3	-0.072	-0.23	0.3	-0.083	-0.23	0.29	-0.072	-0.23	0.3	-0.074	-0.23	0.29	-0.072	-0.22	0.29	-0.076	-0.22	0.29	-0.07	-0.22	0.3	-0.069	-0.23	0.3	-0.073	-0.22
home_player_X1	-0.0034	-0.007	-0.007	0.03	0.03	0.0013	0.00099	0.0022	-0.013	1	0.12	0.032	0.028	-0.018	0.0095	0.013	-0.0088	-0.0027	0.0069	-0.041	0.19	0.031	0.032	0.028	-0.018	0.009	0.013	-0.0049	0.00063	-0.002	nan	0.97	0.96	-4.1e-05	-9.3e-05	0.026	0.0045	0.0036	0.03	0.011	0.0047	-0.12	0.58	nan	-4.1e-05	-5.6e-05	0.019	-0.004	-0.0075	0.0084	-0.00013	0.011	nan	0.0075	0.01	0.0089	0.0092	0.0087	0.009	0.0087	0.012	0.0086	0.0048	0.01	0.0099	0.0075	0.009	0.0085	0.012	0.011	0.0084	0.011	0.015	0.013	0.0077	-0.0039	0.0057	0.0057	-0.0031	0.0046	0.0065	-0.0031	0.0054	0.0071	-0.0023	0.0065	0.0069	-0.0047	-0.0022	0.0015	-0.0027	0.0033	0.0075	-0.0085	0.0035	0.0068	-0.0013	0.0059	0.0073	-0.0056	0.0038	0.0084	-0.0064	0.0049	0.0088
home_player_X2	0.0066	-0.0021	-0.0021	0.02	0.091	0.012	0.00061	0.0067	-0.022	0.12	1	0.54	0.17	-0.68	-0.048	0.15	0.19	0.034	-0.098	0.023	0.0048	0.13	0.096	0.0091	-0.14	0.039	0.02	0.06	-0.0058	-0.036	0.009	0.11	0.11	0.032	0.027	0.61	0.24	0.091	-0.047	0.037	0.092	-0.034	0.017	nan	-0.0012	-0.0017	0.13	0.0093	0.033	-0.052	0.012	0.02	-0.011	0.041	0.0099	0.0095	0.044	0.049	0.015	0.015	0.054	0.029	0.034	0.019	0.036	0.03	0.029	0.025	0.03	0.028	0.024	0.028	0.025	0.036	0.03	-0.021	0.0046	0.019	-0.02	0.0078	0.025	-0.019	0.013	0.02	-0.019	0.012	0.027	-0.026	0.011	0.032	-0.018	-0.0013	0.022	-0.02	-0.024	-0.00043	-0.016	0.0095	0.027	-0.019	-0.0099	0.01	-0.019	-0.012	0.0076
home_player_X3	0.027	0.019	0.019	0.0014	0.089	0.027	0.022	-0.0053	-0.0075	0.032	0.54	1	0.11	-0.51	-0.056	0.14	0.13	-0.047	-0.039	0.0063	0.005	0.08	0.12	-0.01	-0.083	0.034	0.0056	0.034	-0.02	-0.014	0.01	0.032	nan	-0.001	0.057	0.4	0.2	0.059	-0.02	0.081	0.041	0.023	0.017	nan	-0.001	-0.0014	0.071	0.0092	0.033	-0.024	0.02	0.0088	-0.0027	0.034	0.024	0.02	0.053	0.052	0.036	0.028	0.053	0.04	0.044	0.022	0.042	0.043	0.028	0.023	0.04	0.039	0.039	0.03	0.047	0.031	0.035	-0.012	-0.009	0.002	-0.012	-0.0092	0.0063	-0.0097	-0.0012	0.0022	-0.01	-0.0021	0.012	-0.014	-0.016	0.01	-0.011	-0.012	0.0049	-0.013	-0.025	-0.0072	-0.0097	-0.0074	0.0052	-0.014	-0.012	0.00092	-0.015	-0.014	-0.00077
home_player_X4	-0.017	-0.018	-0.018	-0.016	-0.0071	0.00012	0.0043	-0.0066	-0.0069	0.028	0.17	0.11	1	-0.57	-0.11	0.1	0.17	0.056	-0.076	0.052	0.0046	0.0045	-0.021	0.13	-0.11	-0.016	-0.005	0.068	0.026	-0.034	0.024	0.028	nan	-0.00071	-0.027	0.52	0.18	0.046	-0.046	0.022	0.084	-0.037	0.015	nan	-0.00071	-0.00095	0.12	0.0065	0.005	-0.045	-0.0033	0.042	-0.028	0.00051	-0.016	-0.05	-0.026	-0.013	-0.022	-0.049	-0.0032	-0.027	-0.026	-0.024	-0.01	-0.031	-0.021	-0.015	-0.022	-0.016	-0.028	-0.021	-0.032	-0.041	-0.023	-0.016	-0.028	-0.0065	-0.017	-0.024	-0.0018	-0.013	-0.02	-0.0064	-0.016	-0.025	-0.0049	-0.018	-0.026	0.0041	-0.016	-0.028	-0.0021	-0.016	-0.025	-0.0039	-0.016	-0.029	-0.0034	-0.014	-0.02	-0.0013	-0.014	-0.022	-0.0042
home_player_X5	0.046	0.054	0.054	-0.0087	-0.067	-0.0073	-0.0083	0.014	0.01	-0.018	-0.68	-0.51	-0.57	1	-0.092	-0.13	-0.28	-0.13	0.16	-0.051	-0.0014	-0.13	-0.082	-0.098	0.23	-0.031	-0.0081	-0.12	-0.02	0.061	-0.03	-0.018	nan	-0.022	-0.028	-0.94	-0.13	-0.14	0.096	0.018	-0.14	0.071	-0.0084	nan	0.0018	0.0024	-0.23	-0.0049	-0.036	0.095	-0.0047	-0.056	0.035	-0.024	0.01	0.036	-0.0037	-0.02	-0.00019	0.024	-0.028	-0.0014	-0.0051	0.0082	-0.019	0.0098	0.0088	0.0063	-0.0031	-0.004	0.0087	0.0033	0.0063	0.014	-0.0068	0.00076	0.028	0.011	-0.0027	0.021	0.0027	-0.0025	0.013	0.01	-0.002	0.019	0.0055	0.0035	0.025	-0.0093	-0.0033	0.029	0.0057	0.00054	0.042	0.019	-0.0032	0.023	0.0032	-0.00022	0.029	0.012	0.00082	0.031	0.016
home_player_X6	-0.025	-0.05	-0.05	-0.016	0.21	0.0052	0.01	-0.0056	0.0095	0.0095	-0.048	-0.056	-0.11	-0.092	1	0.27	-0.41	-0.41	0.26	-0.19	0.0017	0.033	0.034	-0.016	-0.036	0.36	0.23	-0.21	-0.21	0.12	-0.092	0.0095	nan	0.015	0.038	0.066	-0.78	-0.26	0.38	0.28	-0.23	0.38	0.0074	nan	-0.00097	0.001	0.028	-0.28	-0.21	0.2	0.17	-0.1	0.2	0.094	0.11	0.093	0.096	0.093	0.061	0.12	0.13	0.082	0.13	0.085	0.092	0.097	0.094	0.079	0.089	0.082	0.089	0.1	0.094	0.093	0.081	0.018	0.013	-0.0066	0.027	0.025	-0.00053	0.024	0.029	-2.4e-05	0.022	0.023	-7.2e-05	0.038	0.008	-0.00014	0.025	0.012	0.003	-7.8e-05	0.0069	-0.0039	0.028	0.032	0.0033	0.0017	0.0044	-0.0024	-0.0042	-0.00098	-0.014
home_player_X7	0.082	0.057	0.057	-0.0056	0.21	0.016	0.012	0.028	-0.01	0.013	0.15	0.14	0.1	-0.13	0.27	1	-0.42	-0.45	0.19	-0.12	0.0052	0.032	0.035	0.0014	-0.029	0.23	0.22	-0.14	-0.19	0.086	-0.053	0.013	nan	0.019	0.0057	0.16	-0.13	-0.68	0.4	0.33	-0.19	0.22	0.013	nan	0.0014	0.0045	0.027	-0.16	-0.17	0.15	0.15	-0.073	0.13	0.099	0.11	0.089	0.1	0.096	0.07	0.073	0.14	0.08	0.13	0.066	0.096	0.097	0.083	0.08	0.084	0.088	0.091	0.092	0.095	0.094	0.074	-0.03	0.029	0.024	-0.027	0.035	0.033	-0.025	0.044	0.033	-0.029	0.037	0.031	-0.038	0.0052	0.024	-0.028	0.024	0.032	-0.033	0.04	0.032	-0.023	0.043	0.033	-0.029	0.049	0.041	-0.034	0.043	0.032
home_player_X8	-0.06	-0.048	-0.048	0.0018	-0.097	-0.0011	0.0074	0.019	-0.011	-0.0088	0.19	0.13	0.17	-0.28	-0.41	-0.42	1	-0.058	-0.61	0.69	0.0028	0.048	0.018	0.052	-0.099	-0.21	-0.15	0.32	0.05	-0.19	0.22	-0.0088	nan	-0.013	-0.0043	0.3	0.51	0.6	-0.75	0.28	0.68	-0.61	-0.0064	nan	0.0064	0.0018	0.1	0.22	0.21	-0.28	0.023	0.21	-0.25	-0.026	-0.032	-0.043	-0.033	-0.012	0.011	-0.046	-0.068	0.0051	-0.081	0.029	-0.022	-0.031	-0.021	-0.022	-0.022	-0.021	-0.029	-0.038	-0.024	-0.033	0.014	-0.027	0.035	0.057	-0.032	0.026	0.055	-0.031	0.023	0.053	-0.03	0.025	0.052	-0.022	0.043	0.041	-0.034	0.043	0.049	-0.019	0.021	0.049	-0.032	0.027	0.056	-0.025	0.018	0.051	-0.021	0.026	0.057
home_player_X9	-0.054	-0.027	-0.027	0.028	-0.25	-0.026	-0.025	-0.035	0.0039	-0.0027	0.034	-0.047	0.056	-0.13	-0.41	-0.45	-0.058	1	-0.33	-0.3	-0.015	-0.0034	-0.019	0.023	-0.013	-0.22	-0.19	0.051	0.32	-0.096	-0.069	-0.0027	nan	0.0071	-0.0031	0.11	0.14	0.17	-0.11	-0.9	0.11	-0.15	-0.0084	nan	-0.016	-0.015	0.016	0.11	0.096	-0.11	-0.3	0.038	-0.072	-0.13	-0.17	-0.15	-0.15	-0.16	-0.13	-0.16	-0.16	-0.16	-0.16	-0.19	-0.14	-0.17	-0.16	-0.13	-0.14	-0.14	-0.15	-0.15	-0.16	-0.16	-0.16	0.027	-0.1	-0.084	0.024	-0.1	-0.087	0.023	-0.1	-0.092	0.025	-0.11	-0.089	0.028	-0.11	-0.081	0.029	-0.1	-0.088	0.029	-0.09	-0.081	0.019	-0.12	-0.095	0.031	-0.075	-0.076	0.036	-0.076	-0.07
home_player_X10	0.055	0.038	0.038	-0.0083	0.17	0.017	0.0046	-0.029	0.012	0.0069	-0.098	-0.039	-0.076	0.16	0.26	0.19	-0.61	-0.33	1	-0.63	-0.013	-0.029	-0.01	-0.029	0.054	0.14	0.11	-0.21	-0.12	0.22	-0.15	0.0069	nan	-0.006	-0.0095	-0.16	-0.26	-0.29	0.57	0.076	-0.9	0.77	-0.0054	nan	-0.015	-0.01	-0.056	-0.12	-0.13	0.21	0.056	-0.21	0.22	0.053	0.071	0.069	0.062	0.06	0.03	0.08	0.098	0.048	0.12	0.035	0.055	0.076	0.059	0.053	0.058	0.06	0.068	0.066	0.06	0.065	0.034	0.04	-0.0032	-0.035	0.046	0.0029	-0.034	0.046	0.0047	-0.031	0.045	0.0098	-0.026	0.03	0.0057	-0.014	0.047	-0.012	-0.026	0.041	-0.0092	-0.035	0.046	0.012	-0.027	0.041	-0.019	-0.045	0.037	-0.023	-0.049
home_player_X11	-0.023	-0.016	-0.016	-0.0076	-0.05	0.0028	0.012	0.05	-0.014	-0.041	0.023	0.0063	0.052	-0.051	-0.19	-0.12	0.69	-0.3	-0.63	1	-0.03	-0.012	-0.0088	0.017	-0.019	-0.089	-0.059	0.22	-0.066	-0.14	0.24	-0.041	nan	0.0018	0.0041	0.059	0.35	0.39	-0.49	0.54	0.77	-0.77	-0.056	nan	-0.015	-0.016	0.021	0.13	0.13	-0.16	0.13	0.18	-0.21	0.0054	0.025	0.023	0.017	0.034	0.051	0.016	-0.019	0.049	-0.043	0.097	0.024	0.023	0.036	0.029	0.028	0.029	0.026	0.023	0.039	0.03	0.067	-0.051	0.091	0.1	-0.054	0.082	0.1	-0.054	0.074	0.1	-0.053	0.077	0.096	-0.037	0.088	0.078	-0.059	0.1	0.095	-0.049	0.086	0.1	-0.052	0.088	0.1	-0.057	0.078	0.1	-0.055	0.086	0.11
away_player_X1	0.0061	0.0063	0.0063	0.0057	-0.0031	-0.00034	-0.0028	-0.0065	0.015	0.19	0.0048	0.005	0.0046	-0.0014	0.0017	0.0052	0.0028	-0.015	-0.013	-0.03	1	0.1	-0.0045	-0.0038	-0.0081	0.0035	0.002	0.0051	0.0032	-0.0028	nan	0.19	nan	-5.6e-05	-0.00012	0.0031	0.0045	0.0027	0.0049	-0.036	-0.033	-0.14	0.78	nan	0.96	0.86	0.031	0.027	0.036	0.033	-0.00018	0.015	nan	0.0059	-0.0043	0.013	-0.003	-0.0063	-0.00093	0.0049	0.0095	-0.0064	-0.0052	0.0036	0.0088	0.0015	0.0044	0.0027	0.0017	-0.0032	-0.00099	0.0012	0.0044	-0.00076	0.0098	-0.0021	-0.0046	-0.003	-0.0018	-0.0047	-0.0029	-0.0022	-0.005	-0.0031	-0.0012	-0.0062	-0.0039	-0.0047	-0.0022	0.0015	-0.0014	-0.0053	-0.0033	-0.002	-0.0058	-0.0035	-0.0017	-0.004	-0.0028	-0.0021	-0.006	-0.004	-0.002	-0.0061	-0.0041
away_player_X2	0.0055	-0.0025	-0.0025	0.018	0.089	-0.00056	0.013	-0.018	0.018	0.031	0.13	0.08	0.0045	-0.13	0.033	0.032	0.048	-0.0034	-0.029	-0.012	0.1	1	0.56	0.16	-0.67	-0.072	0.17	0.18	0.042	-0.1	0.018	0.031	nan	-0.0012	0.012	0.12	0.017	0.028	-0.046	-0.002	0.011	-0.02	0.09	nan	0.094	0.098	0.6	0.27	0.086	-0.039	0.028	0.089	-0.024	0.033	0.014	0.016	0.028	0.019	0.031	0.019	0.021	0.033	0.022	0.025	0.039	0.021	1.6e-05	0.034	0.038	0.03	0.0033	0.043	0.035	0.028	0.019	0.026	-0.02	-0.026	0.026	-0.018	-0.021	0.028	-0.014	-0.024	0.031	-0.014	-0.023	0.037	-0.029	-0.033	0.031	-0.025	-0.026	0.0051	-0.036	-0.022	0.03	-0.02	-0.022	0.004	-0.031	-0.017	0.0032	-0.031	-0.017
away_player_X3	0.015	0.0067	0.0067	0.0085	0.087	0.0057	0.018	-0.014	-0.0018	0.032	0.096	0.12	-0.021	-0.082	0.034	0.035	0.018	-0.019	-0.01	-0.0088	-0.0045	0.56	1	0.18	-0.52	-0.089	0.14	0.15	-0.041	-0.052	0.013	0.032	nan	-0.00097	0.042	0.068	0.0047	0.016	-0.0074	0.015	0.0043	-0.004	0.027	nan	-0.018	0.014	0.41	0.22	0.057	-0.019	0.086	0.055	0.023	0.041	0.035	0.028	0.036	0.041	0.046	0.032	0.038	0.028	0.037	0.032	0.043	0.026	0.0089	0.049	0.047	0.038	0.023	0.053	0.041	0.035	0.024	0.013	-0.024	-0.025	0.014	-0.024	-0.022	0.014	-0.019	-0.023	0.016	-0.021	-0.022	0.014	-0.034	-0.027	0.015	-0.03	-0.026	0.0019	-0.03	-0.021	0.016	-0.023	-0.02	0.0038	-0.032	-0.024	0.0034	-0.029	-0.023
away_player_X4	-0.02	-0.021	-0.021	-0.0074	-0.0047	-0.011	-0.0031	-0.016	-0.009	0.028	0.0091	-0.01	0.13	-0.098	-0.016	0.0014	0.052	0.023	-0.029	0.017	-0.0038	0.16	0.18	1	-0.57	-0.13	0.11	0.18	0.069	-0.092	0.046	0.028	nan	-0.00075	-0.04	0.1	0.0024	-0.013	-0.038	-0.0054	0.03	-0.031	0.024	nan	-0.015	-0.02	0.53	0.22	0.048	-0.027	0.021	0.1	-0.04	-0.0004	-0.03	-0.033	-0.023	-0.02	-0.033	-0.026	-0.016	-0.017	-0.031	-0.033	-0.0099	-0.035	-0.047	-0.021	-0.0057	-0.017	-0.037	-0.004	-0.017	-0.028	-0.015	0.001	-0.044	-0.03	0.0046	-0.043	-0.031	0.0052	-0.036	-0.026	0.00015	-0.045	-0.034	0.0029	-0.049	-0.031	0.003	-0.046	-0.03	0.0041	-0.032	-0.018	0.0032	-0.048	-0.034	0.0047	-0.031	-0.019	0.0037	-0.03	-0.02
away_player_X5	0.045	0.054	0.054	-0.023	-0.074	-0.0028	-0.012	0.0073	0.01	-0.018	-0.14	-0.083	-0.11	0.23	-0.036	-0.029	-0.099	-0.013	0.054	-0.019	-0.0081	-0.67	-0.52	-0.57	1	-0.091	-0.13	-0.29	-0.14	0.16	-0.042	-0.018	nan	0.0019	0.00065	-0.23	-0.0059	-0.021	0.079	-0.0054	-0.048	0.031	-0.019	nan	-0.0021	-0.012	-0.94	-0.13	-0.14	0.095	0.028	-0.14	0.067	-0.021	0.016	0.022	0.0031	0.0039	0.005	0.013	-0.00019	0.0008	0.0085	0.0053	-0.026	0.0091	0.035	-0.006	-0.012	-0.012	0.021	-0.026	-0.012	-0.0017	-0.0052	0.012	0.018	-0.0066	0.0096	0.011	-0.0098	0.011	0.0026	-0.0084	0.01	0.011	-0.0071	0.004	0.03	-0.0033	0.0079	0.021	-0.0071	0.021	0.024	-0.009	0.0064	0.018	-0.0071	0.02	0.013	-0.015	0.02	0.014	-0.011
away_player_X6	-0.014	-0.04	-0.04	-0.013	0.23	0.021	0.0093	0.021	-0.0028	0.009	0.039	0.034	-0.016	-0.031	0.36	0.23	-0.21	-0.22	0.14	-0.089	0.0035	-0.072	-0.089	-0.13	-0.091	1	0.23	-0.39	-0.39	0.22	-0.18	0.009	nan	0.009	0.014	0.029	-0.27	-0.2	0.2	0.18	-0.11	0.19	0.013	nan	-0.00098	0.0054	0.058	-0.8	-0.24	0.38	0.26	-0.2	0.36	0.09	0.093	0.084	0.087	0.088	0.082	0.089	0.11	0.093	0.097	0.078	0.1	0.11	0.097	0.093	0.1	0.063	0.13	0.12	0.094	0.13	0.087	-0.021	0.039	0.032	-0.016	0.053	0.041	-0.016	0.055	0.038	-0.017	0.051	0.04	-0.049	0.05	0.075	-0.015	0.039	0.041	-0.018	0.029	0.022	-0.013	0.06	0.047	-0.0083	0.027	0.018	-0.011	0.022	0.00056
away_player_X7	0.086	0.063	0.063	-0.0085	0.2	0.024	0.011	-0.0047	0.028	0.013	0.02	0.0056	-0.005	-0.0081	0.23	0.22	-0.15	-0.19	0.11	-0.059	0.002	0.17	0.14	0.11	-0.13	0.23	1	-0.39	-0.44	0.1	-0.081	0.013	nan	-0.016	-0.0044	0.0064	-0.17	-0.18	0.15	0.15	-0.098	0.13	0.0026	nan	0.0015	-0.00059	0.15	-0.097	-0.7	0.4	0.34	-0.12	0.18	0.089	0.095	0.082	0.087	0.079	0.078	0.092	0.086	0.081	0.096	0.072	0.092	0.1	0.079	0.097	0.089	0.076	0.068	0.13	0.083	0.12	0.073	0.028	-0.011	-0.043	0.034	-0.0056	-0.033	0.037	-0.001	-0.037	0.034	-0.0079	-0.04	0.0078	-0.023	-0.03	0.034	-0.016	-0.038	0.03	0.0018	-0.033	0.034	0.0044	-0.029	0.041	-0.0039	-0.038	0.038	-0.008	-0.054
away_player_X8	-0.055	-0.045	-0.045	0.0077	-0.083	-0.00011	0.012	-0.016	0.023	-0.0049	0.06	0.034	0.068	-0.12	-0.21	-0.14	0.32	0.051	-0.21	0.22	0.0051	0.18	0.15	0.18	-0.29	-0.39	-0.39	1	-0.068	-0.59	0.69	-0.0049	nan	-0.0049	-0.0076	0.12	0.22	0.2	-0.27	0.023	0.22	-0.25	0.00026	nan	0.0065	0.0053	0.31	0.51	0.6	-0.75	0.29	0.67	-0.62	-0.013	-0.024	-0.017	-0.027	-0.014	-0.0025	-0.028	-0.024	-0.012	-0.027	0.01	-0.021	-0.023	-0.028	-0.025	-0.017	0.0077	-0.04	-0.059	0.011	-0.082	0.04	0.061	-0.02	-0.037	0.058	-0.03	-0.042	0.057	-0.03	-0.038	0.053	-0.03	-0.037	0.063	-0.02	-0.044	0.053	-0.013	-0.045	0.059	-0.03	-0.038	0.057	-0.032	-0.042	0.056	-0.027	-0.032	0.058	-0.022	-0.023
away_player_X9	-0.056	-0.029	-0.029	0.034	-0.25	-0.029	-0.016	-0.00066	-0.034	0.00063	-0.0058	-0.02	0.026	-0.02	-0.21	-0.19	0.05	0.32	-0.12	-0.066	0.0032	0.042	-0.041	0.069	-0.14	-0.39	-0.44	-0.068	1	-0.29	-0.31	0.00063	nan	0.00063	-0.0045	0.028	0.11	0.1	-0.094	-0.3	0.053	-0.078	0.0032	nan	nan	0.00063	0.13	0.13	0.16	-0.12	-0.9	0.082	-0.13	-0.13	-0.16	-0.15	-0.14	-0.14	-0.15	-0.15	-0.15	-0.15	-0.15	-0.15	-0.13	-0.17	-0.16	-0.14	-0.16	-0.13	-0.16	-0.16	-0.16	-0.15	-0.18	-0.074	-0.034	0.03	-0.078	-0.034	0.025	-0.081	-0.034	0.022	-0.074	-0.033	0.023	-0.061	-0.021	0.031	-0.075	-0.034	0.029	-0.069	-0.035	0.021	-0.079	-0.052	0.015	-0.074	-0.028	0.022	-0.071	-0.026	0.031
away_player_X10	0.038	0.024	0.024	-0.015	0.13	0.003	-0.012	0.015	-0.037	-0.002	-0.036	-0.014	-0.034	0.061	0.12	0.086	-0.19	-0.096	0.22	-0.14	-0.0028	-0.1	-0.052	-0.092	0.16	0.22	0.1	-0.59	-0.29	1	-0.63	-0.002	nan	0.011	0.0068	-0.061	-0.11	-0.1	0.18	0.039	-0.21	0.21	-0.0028	nan	nan	-0.0062	-0.17	-0.26	-0.25	0.53	0.029	-0.9	0.79	0.029	0.042	0.035	0.041	0.034	0.037	0.048	0.039	0.036	0.037	0.011	0.028	0.054	0.049	0.036	0.05	0.024	0.069	0.076	0.019	0.1	0.01	-0.056	0.051	0.072	-0.055	0.057	0.073	-0.053	0.06	0.073	-0.051	0.059	0.072	-0.047	0.036	0.05	-0.049	0.043	0.075	-0.059	0.056	0.081	-0.05	0.062	0.074	-0.065	0.058	0.082	-0.066	0.056	0.082
away_player_X11	-0.014	-0.0098	-0.0098	-0.003	-0.028	0.0088	0.02	-0.011	0.049	nan	0.009	0.01	0.024	-0.03	-0.092	-0.053	0.22	-0.069	-0.15	0.24	nan	0.018	0.013	0.046	-0.042	-0.18	-0.081	0.69	-0.31	-0.63	1	nan	nan	-0.0065	-0.0069	0.026	0.14	0.13	-0.16	0.14	0.19	-0.21	nan	nan	nan	0.002	0.058	0.36	0.38	-0.48	0.56	0.78	-0.81	0.033	0.044	0.048	0.029	0.046	0.045	0.031	0.034	0.047	0.036	0.073	0.022	0.04	0.041	0.023	0.041	0.05	0.017	-0.0057	0.067	-0.038	0.11	0.11	-0.0086	-0.073	0.11	-0.018	-0.076	0.11	-0.025	-0.072	0.1	-0.022	-0.071	0.097	-0.0021	-0.06	0.1	0.002	-0.078	0.11	-0.02	-0.079	0.11	-0.012	-0.07	0.12	-0.027	-0.087	0.12	-0.023	-0.084
home_player_Y1	-0.0032	-0.0066	-0.0066	0.025	0.027	0.00097	0.00085	0.0013	-0.011	0.97	0.11	0.032	0.028	-0.018	0.0095	0.013	-0.0088	-0.0027	0.0069	-0.041	0.19	0.031	0.032	0.028	-0.018	0.009	0.013	-0.0049	0.00063	-0.002	nan	1	0.86	-4.1e-05	-9.3e-05	0.026	0.0045	0.0036	0.03	0.011	0.0047	-0.12	0.58	nan	-4.1e-05	-5.6e-05	0.019	-0.004	-0.0075	0.0084	-0.00013	0.011	nan	0.006	0.0085	0.0081	0.007	0.0065	0.007	0.0072	0.011	0.0064	0.0037	0.01	0.011	0.0057	0.0072	0.0066	0.0098	0.0088	0.0066	0.011	0.015	0.012	0.0058	-0.0043	0.0045	0.0053	-0.0037	0.0038	0.0061	-0.0036	0.0043	0.0063	-0.0028	0.005	0.0059	-0.0047	-0.0022	0.0015	-0.0033	0.0025	0.007	-0.0086	0.0027	0.0063	-0.002	0.0051	0.0068	-0.0062	0.0032	0.0079	-0.0068	0.004	0.0081
home_player_Y2	-0.0034	-0.007	-0.007	0.034	0.031	0.0016	0.0011	0.0031	-0.015	0.96	0.11	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.86	1	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.0087	0.011	0.0089	0.011	0.011	0.011	0.0097	0.011	0.01	0.0058	0.0092	0.0078	0.0091	0.01	0.01	0.013	0.013	0.0098	0.0099	0.013	0.012	0.0092	-0.003	0.0065	0.0058	-0.0022	0.0052	0.0064	-0.0023	0.0061	0.0075	-0.0015	0.0077	0.0076	nan	nan	nan	-0.0019	0.004	0.0076	-0.0076	0.0043	0.0068	-0.00033	0.0064	0.0074	-0.0045	0.0043	0.0082	-0.0054	0.0056	0.0089
home_player_Y3	-0.0034	-0.0031	-0.0031	-0.00034	-0.0026	-0.0008	-0.00078	-0.0027	-0.0066	-4.1e-05	0.032	-0.001	-0.00071	-0.022	0.015	0.019	-0.013	0.0071	-0.006	0.0018	-5.6e-05	-0.0012	-0.00097	-0.00075	0.0019	0.009	-0.016	-0.0049	0.00063	0.011	-0.0065	-4.1e-05	nan	1	0.45	-0.0016	-0.03	-0.04	-0.0026	-0.0059	0.0047	-0.0056	-7.2e-05	nan	-4.1e-05	-5.6e-05	-0.0017	-0.013	0.0035	-0.0027	-0.0058	-0.013	0.007	0.0075	-0.0055	-0.004	-0.0036	0.011	0.0026	-0.0038	0.0075	0.0042	-0.0051	-0.004	-0.0036	-0.0046	-0.004	-2.9e-05	-0.0037	-0.0038	-0.0036	-0.0049	-0.0041	-0.0046	-0.00058	-0.0022	-0.0027	-0.0019	-0.0017	-0.0023	-0.0018	-0.0022	-0.0026	-0.002	-0.002	-0.0021	-0.0018	nan	nan	nan	-0.0015	-0.0029	-0.0022	-0.0024	-0.002	-0.0024	-0.0018	-0.0027	-0.0017	-0.0023	-0.0039	-0.0027	-0.0026	-0.0026	-0.0024
home_player_Y4	-0.01	-0.01	-0.01	0.00034	-0.0019	-0.0011	-0.0009	0.00051	-0.0047	-9.3e-05	0.027	0.057	-0.027	-0.028	0.038	0.0057	-0.0043	-0.0031	-0.0095	0.0041	-0.00012	0.012	0.042	-0.04	0.00065	0.014	-0.0044	-0.0076	-0.0045	0.0068	-0.0069	-9.3e-05	nan	0.45	1	-0.0036	-0.068	-0.012	0.014	-0.0029	0.016	0.01	-0.00016	nan	-9.3e-05	-0.00012	-0.0037	-0.0089	0.0029	0.0088	0.0023	-0.018	0.01	-0.00016	-0.01	0.0042	-0.0059	0.0081	-0.0056	-0.0089	0.002	0.002	-0.00026	5.3e-05	-0.001	0.00018	-0.0089	9.4e-05	0.0036	-0.0053	-0.0021	-0.0024	-0.00033	-0.00036	-0.00092	-0.0072	-0.00058	0.0042	-0.0071	0.00042	0.006	-0.0076	0.0011	0.0043	-0.0071	-0.00054	0.0045	-0.0083	0.0041	0.014	-0.0067	-0.00029	0.0045	-0.0082	1.3e-05	0.0049	-0.0066	-0.00068	0.0061	-0.0046	-0.0068	-0.0036	-0.0046	-0.0056	-0.0039
home_player_Y5	-0.049	-0.056	-0.056	0.019	0.059	-0.00091	0.0078	-0.0075	-0.0083	0.026	0.61	0.4	0.52	-0.94	0.066	0.16	0.3	0.11	-0.16	0.059	0.0031	0.12	0.068	0.1	-0.23	0.029	0.0064	0.12	0.028	-0.061	0.026	0.026	nan	-0.0016	-0.0036	1	0.16	0.14	-0.098	0.0025	0.16	-0.066	0.013	nan	-0.0016	-0.0022	0.23	0.0009	0.031	-0.098	-0.002	0.056	-0.032	0.021	-0.015	-0.051	-0.0087	0.006	-0.0089	-0.035	0.024	-0.0075	-0.0034	-0.014	0.012	-0.018	-0.019	-0.016	-0.0084	-0.0048	-0.017	-0.012	-0.02	-0.025	-0.0043	-0.01	-0.024	-0.0034	-0.0073	-0.016	0.0056	-0.0076	-0.0074	-0.0017	-0.008	-0.015	0.0016	-0.014	-0.023	0.014	-0.0061	-0.024	0.0022	-0.0086	-0.034	-0.0097	-0.0066	-0.02	0.0038	-0.0065	-0.02	-0.003	-0.007	-0.023	-0.0065
home_player_Y6	0.069	0.076	0.076	0.015	-0.016	0.014	0.01	0.023	-0.007	0.0045	0.24	0.2	0.18	-0.13	-0.78	-0.13	0.51	0.14	-0.26	0.35	0.0045	0.017	0.0047	0.0024	-0.0059	-0.27	-0.17	0.22	0.11	-0.11	0.14	0.0045	nan	-0.03	-0.068	0.16	1	0.52	-0.18	0.047	0.29	-0.4	0.0029	nan	0.0045	0.0022	0.0056	0.31	0.25	-0.14	-0.057	0.12	-0.2	-0.00048	0.0081	-0.0013	0.01	0.018	0.041	-0.0061	-0.0061	0.031	-0.024	0.044	0.0076	0.021	0.019	0.019	0.022	0.023	0.014	0.0054	0.012	0.017	0.033	-0.018	0.047	0.039	-0.025	0.033	0.035	-0.02	0.027	0.033	-0.017	0.045	0.041	-0.051	0.039	0.044	-0.023	0.046	0.035	-0.0017	0.039	0.029	-0.022	0.038	0.038	-0.00091	0.04	0.022	0.0039	0.046	0.033
home_player_Y7	-0.014	-0.0058	-0.0058	0.0015	-0.03	0.0013	0.0064	0.00088	0.0091	0.0036	0.091	0.059	0.046	-0.14	-0.26	-0.68	0.6	0.17	-0.29	0.39	0.0027	0.028	0.016	-0.013	-0.021	-0.2	-0.18	0.2	0.1	-0.1	0.13	0.0036	nan	-0.04	-0.012	0.14	0.52	1	-0.21	0.05	0.36	-0.34	-0.00011	nan	0.0036	-8.2e-05	0.017	0.23	0.26	-0.12	-0.046	0.11	-0.17	-0.0099	-0.001	-0.0023	-0.00067	0.016	0.04	0.027	-0.026	0.033	-0.036	0.053	0.0078	0.011	0.025	0.013	0.024	0.021	0.013	0.0069	0.0091	0.015	0.037	0.016	0.042	0.024	0.012	0.03	0.016	0.014	0.018	0.014	0.018	0.037	0.023	0.013	0.049	0.03	0.013	0.044	0.019	0.02	0.016	0.0047	0.012	0.035	0.023	0.017	0.01	-0.0061	0.021	0.017	0.0037
home_player_Y8	0.1	0.08	0.08	-0.0021	0.22	0.0062	0.0029	-0.00046	0.011	0.03	-0.047	-0.02	-0.046	0.096	0.38	0.4	-0.75	-0.11	0.57	-0.49	0.0049	-0.046	-0.0074	-0.038	0.079	0.2	0.15	-0.27	-0.094	0.18	-0.16	0.03	nan	-0.0026	0.014	-0.098	-0.18	-0.21	1	-0.0028	-0.57	0.56	0.021	nan	-0.0026	0.0014	-0.086	-0.14	-0.12	0.32	0.044	-0.18	0.22	0.083	0.11	0.1	0.099	0.087	0.064	0.11	0.14	0.078	0.14	0.058	0.085	0.11	0.089	0.084	0.097	0.094	0.098	0.11	0.094	0.099	0.066	0.017	0.025	-0.017	0.022	0.028	-0.015	0.022	0.028	-0.015	0.023	0.038	-0.0069	0.00087	-0.002	0.003	0.023	0.016	-0.0062	0.014	0.026	-0.02	0.026	0.037	-0.0097	0.021	0.03	-0.027	0.018	0.025	-0.031
home_player_Y9	0.033	0.0091	0.0091	-0.03	0.23	0.029	0.025	0.049	-0.0087	0.011	0.037	0.081	0.022	0.018	0.28	0.33	0.28	-0.9	0.076	0.54	-0.036	-0.002	0.015	-0.0054	-0.0054	0.18	0.15	0.023	-0.3	0.039	0.14	0.011	nan	-0.0059	-0.0029	0.0025	0.047	0.05	-0.0028	1	0.18	-0.092	-0.017	nan	-0.04	-0.036	0.0027	-0.059	-0.04	0.048	0.31	0.029	0.00032	0.12	0.17	0.14	0.14	0.15	0.14	0.15	0.15	0.16	0.13	0.2	0.14	0.16	0.16	0.13	0.15	0.14	0.15	0.15	0.16	0.15	0.16	-0.039	0.12	0.1	-0.038	0.11	0.1	-0.036	0.11	0.11	-0.037	0.12	0.1	-0.038	0.11	0.093	-0.043	0.12	0.1	-0.039	0.1	0.096	-0.032	0.13	0.11	-0.043	0.088	0.092	-0.047	0.091	0.087
home_player_Y10	-0.054	-0.041	-0.041	0.0016	-0.12	-0.0072	0.0015	0.043	-0.014	0.0047	0.092	0.041	0.084	-0.14	-0.23	-0.19	0.68	0.11	-0.9	0.77	-0.033	0.011	0.0043	0.03	-0.048	-0.11	-0.098	0.22	0.053	-0.21	0.19	0.0047	nan	0.0047	0.016	0.16	0.29	0.36	-0.57	0.18	1	-0.81	-0.013	nan	-0.037	-0.037	0.05	0.12	0.13	-0.2	0.015	0.22	-0.23	-0.032	-0.039	-0.037	-0.035	-0.026	-0.0034	-0.048	-0.067	-0.015	-0.096	0.017	-0.021	-0.039	-0.021	-0.021	-0.021	-0.027	-0.034	-0.033	-0.023	-0.032	0.0039	-0.056	0.035	0.064	-0.06	0.027	0.063	-0.06	0.024	0.063	-0.06	0.02	0.056	-0.047	0.031	0.045	-0.064	0.046	0.054	-0.052	0.038	0.062	-0.06	0.023	0.058	-0.054	0.039	0.068	-0.051	0.045	0.071
home_player_Y11	0.018	-5.8e-05	-5.8e-05	-0.00021	0.16	0.0098	-0.0043	-0.037	0.012	-0.12	-0.034	0.023	-0.037	0.071	0.38	0.22	-0.61	-0.15	0.77	-0.77	-0.14	-0.02	-0.004	-0.031	0.031	0.19	0.13	-0.25	-0.078	0.21	-0.21	-0.12	nan	-0.0056	0.01	-0.066	-0.4	-0.34	0.56	-0.092	-0.81	1	-0.19	nan	-0.095	-0.081	-0.034	-0.19	-0.18	0.22	0.009	-0.22	0.27	0.043	0.045	0.033	0.041	0.032	-0.0028	0.049	0.076	0.016	0.1	-0.021	0.024	0.043	0.028	0.017	0.024	0.029	0.036	0.032	0.022	0.032	-0.0036	0.039	-0.047	-0.07	0.044	-0.038	-0.066	0.045	-0.03	-0.066	0.043	-0.03	-0.06	0.034	-0.052	-0.055	0.048	-0.058	-0.058	0.029	-0.047	-0.065	0.045	-0.034	-0.064	0.038	-0.042	-0.064	0.034	-0.048	-0.069
away_player_Y1	0.0071	0.0072	0.0072	0.0037	-0.0037	-0.0006	-0.002	-0.0019	0.0081	0.58	0.017	0.017	0.015	-0.0084	0.0074	0.013	-0.0064	-0.0084	-0.0054	-0.056	0.78	0.09	0.027	0.024	-0.019	0.013	0.0026	0.00026	0.0032	-0.0028	nan	0.58	nan	-7.2e-05	-0.00016	0.013	0.0029	-0.00011	0.021	-0.017	-0.013	-0.19	1	nan	0.58	0.52	0.036	0.013	0.025	0.027	-0.00018	0.015	nan	0.00073	-0.0043	0.0067	-0.0057	-0.0079	-0.0022	0.0049	0.0061	-0.0077	-0.0052	0.0025	0.012	-0.0028	-0.00056	-0.0017	-0.0021	-0.0019	-0.002	0.0051	0.0048	-0.00076	0.0021	-0.0041	-0.0049	-0.0021	-0.004	-0.0043	-0.0018	-0.0038	-0.005	-0.0029	-0.0031	-0.0062	-0.0035	-0.0047	-0.0022	0.0015	-0.0033	-0.0053	-0.0023	-0.0043	-0.0057	-0.0026	-0.0036	-0.0038	-0.0018	-0.0048	-0.0057	-0.0023	-0.0045	-0.0056	-0.0029
away_player_Y2	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
away_player_Y3	0.0048	0.0051	0.0051	0.0058	-0.0025	-0.00018	-0.0027	-0.0077	0.016	-4.1e-05	-0.0012	-0.001	-0.00071	0.0018	-0.00097	0.0014	0.0064	-0.016	-0.015	-0.015	0.96	0.094	-0.018	-0.015	-0.0021	-0.00098	0.0015	0.0065	nan	nan	nan	-4.1e-05	nan	-4.1e-05	-9.3e-05	-0.0016	0.0045	0.0036	-0.0026	-0.04	-0.037	-0.095	0.58	nan	1	0.89	0.025	0.03	0.036	0.031	nan	nan	nan	0.0074	nan	0.013	-0.0014	-0.0047	-0.00026	nan	0.0097	-0.005	-0.0044	0.0036	0.0064	0.0032	0.006	0.0042	0.003	-0.0033	-0.00042	-0.00063	0.0036	nan	0.012	-0.00095	-0.0038	-0.003	-0.00067	-0.0042	-0.003	-0.0013	-0.0043	-0.0027	-0.00017	-0.0054	-0.0035	nan	nan	nan	-0.00034	-0.0047	-0.0033	-0.00079	-0.0051	-0.0035	-0.00061	-0.0035	-0.0029	-0.00059	-0.0054	-0.0041	-0.0007	-0.0055	-0.0041
away_player_Y4	0.002	0.002	0.002	0.0067	-0.0008	-0.00023	-0.0025	-0.0081	0.016	-5.6e-05	-0.0017	-0.0014	-0.00095	0.0024	0.001	0.0045	0.0018	-0.015	-0.01	-0.016	0.86	0.098	0.014	-0.02	-0.012	0.0054	-0.00059	0.0053	0.00063	-0.0062	0.002	-5.6e-05	nan	-5.6e-05	-0.00012	-0.0022	0.0022	-8.2e-05	0.0014	-0.036	-0.037	-0.081	0.52	nan	0.89	1	0.022	0.014	0.034	0.036	-0.0058	0.011	0.007	0.0054	0.0086	0.011	0.0015	-0.006	0.0061	-0.0031	0.0067	-0.00075	-0.0043	0.0033	0.0087	0.0011	0.0039	0.0062	0.0076	-0.00019	0.0011	-0.0024	0.005	0.0035	0.0085	-0.00023	-0.0057	-0.0044	-7.3e-05	-0.0057	-0.0043	-0.00045	-0.0057	-0.0042	0.0005	-0.0067	-0.0048	0.0014	-0.0063	-0.005	0.00022	-0.0056	-0.0046	0.00018	-0.0072	-0.0052	0.00011	-0.0048	-0.0042	-0.00059	-0.0054	-0.0041	-0.0007	-0.0055	-0.0041
away_player_Y5	-0.044	-0.053	-0.053	0.028	0.065	0.0037	0.0065	-0.0095	-0.0057	0.019	0.13	0.071	0.12	-0.23	0.028	0.027	0.1	0.016	-0.056	0.021	0.031	0.6	0.41	0.53	-0.94	0.058	0.15	0.31	0.13	-0.17	0.058	0.019	nan	-0.0017	-0.0037	0.23	0.0056	0.017	-0.086	0.0027	0.05	-0.034	0.036	nan	0.025	0.022	1	0.17	0.14	-0.099	-0.0026	0.16	-0.066	0.014	-0.022	-0.031	-0.012	-0.013	-0.013	-0.019	-0.0079	-0.013	-0.018	-0.013	0.021	-0.0097	-0.049	-0.0016	0.0031	0.0037	-0.028	0.02	0.00096	-0.0045	-0.00035	-0.011	-0.025	-0.00099	-0.0077	-0.018	0.0025	-0.0089	-0.01	0.00078	-0.0089	-0.019	-0.00084	-0.0061	-0.037	-0.005	-0.006	-0.027	-0.00099	-0.016	-0.028	0.0027	-0.0052	-0.025	-0.00061	-0.013	-0.015	0.01	-0.014	-0.017	0.0063
away_player_Y6	0.06	0.068	0.068	0.0096	-0.028	-0.0029	0.012	-0.019	0.023	-0.004	0.0093	0.0092	0.0065	-0.0049	-0.28	-0.16	0.22	0.11	-0.12	0.13	0.027	0.27	0.22	0.22	-0.13	-0.8	-0.097	0.51	0.13	-0.26	0.36	-0.004	nan	-0.013	-0.0089	0.0009	0.31	0.23	-0.14	-0.059	0.12	-0.19	0.013	nan	0.03	0.014	0.17	1	0.48	-0.19	0.063	0.3	-0.4	0.0028	0.014	0.015	0.0087	0.021	0.018	0.013	-1.2e-05	0.014	0.0037	0.033	-0.011	0.0099	-0.0061	0.0027	0.0097	0.04	-0.017	-0.012	0.024	-0.024	0.037	0.057	-0.0084	-0.042	0.053	-0.024	-0.05	0.053	-0.026	-0.045	0.057	-0.016	-0.044	0.078	-0.048	-0.089	0.053	-0.012	-0.05	0.044	-0.0033	-0.028	0.054	-0.022	-0.05	0.035	0.0029	-0.023	0.038	0.0063	-0.009
away_player_Y7	-0.017	-0.0097	-0.0097	0.003	-0.019	-0.0084	0.0086	0.0012	0.0019	-0.0075	0.033	0.033	0.005	-0.036	-0.21	-0.17	0.21	0.096	-0.13	0.13	0.036	0.086	0.057	0.048	-0.14	-0.24	-0.7	0.6	0.16	-0.25	0.38	-0.0075	nan	0.0035	0.0029	0.031	0.25	0.26	-0.12	-0.04	0.13	-0.18	0.025	nan	0.036	0.034	0.14	0.48	1	-0.23	0.044	0.33	-0.34	0.0062	0.016	0.018	0.0074	0.031	0.024	0.011	0.012	0.022	0.007	0.038	-0.0026	0.013	0.0094	-0.0019	0.014	0.03	0.032	-0.024	0.036	-0.03	0.055	0.031	0.032	0.0089	0.025	0.021	0.00055	0.025	0.015	0.0062	0.03	0.027	0.01	0.044	0.016	-0.0064	0.026	0.032	0.0046	0.018	0.016	0.0056	0.029	0.02	0.0026	0.011	0.023	0.0097	0.014	0.026	0.023
away_player_Y8	0.096	0.074	0.074	-0.0096	0.21	0.011	0.0041	0.017	-0.0062	0.0084	-0.052	-0.024	-0.045	0.095	0.2	0.15	-0.28	-0.11	0.21	-0.16	0.033	-0.039	-0.019	-0.027	0.095	0.38	0.4	-0.75	-0.12	0.53	-0.48	0.0084	nan	-0.0027	0.0088	-0.098	-0.14	-0.12	0.32	0.048	-0.2	0.22	0.027	nan	0.031	0.036	-0.099	-0.19	-0.23	1	-0.00048	-0.54	0.56	0.077	0.11	0.086	0.095	0.093	0.078	0.1	0.1	0.092	0.095	0.073	0.081	0.1	0.09	0.088	0.097	0.066	0.11	0.13	0.074	0.14	0.054	-0.022	0.042	0.02	-0.02	0.049	0.026	-0.016	0.046	0.022	-0.012	0.052	0.023	-0.038	0.016	0.027	-0.013	0.033	0.028	-0.023	0.051	0.024	-0.015	0.057	0.031	-0.021	0.054	0.019	-0.021	0.048	0.011
away_player_Y9	0.035	0.01	0.01	-0.028	0.24	0.032	0.027	-0.0044	0.045	-0.00013	0.012	0.02	-0.0033	-0.0047	0.17	0.15	0.023	-0.3	0.056	0.13	-0.00018	0.028	0.086	0.021	0.028	0.26	0.34	0.29	-0.9	0.029	0.56	-0.00013	nan	-0.0058	0.0023	-0.002	-0.057	-0.046	0.044	0.31	0.015	0.009	-0.00018	nan	nan	-0.0058	-0.0026	0.063	0.044	-0.00048	1	0.21	-0.13	0.14	0.16	0.15	0.14	0.15	0.15	0.15	0.15	0.16	0.15	0.16	0.12	0.18	0.15	0.14	0.17	0.14	0.15	0.15	0.17	0.13	0.2	0.095	0.024	-0.052	0.099	0.022	-0.048	0.1	0.02	-0.043	0.095	0.02	-0.044	0.078	0.008	-0.05	0.093	0.026	-0.052	0.091	0.025	-0.044	0.1	0.04	-0.037	0.099	0.018	-0.047	0.096	0.018	-0.053
away_player_Y10	-0.037	-0.026	-0.026	0.0056	-0.094	0.0041	0.016	-0.014	0.048	0.011	0.02	0.0088	0.042	-0.056	-0.1	-0.073	0.21	0.038	-0.21	0.18	0.015	0.089	0.055	0.1	-0.14	-0.2	-0.12	0.67	0.082	-0.9	0.78	0.011	nan	-0.013	-0.018	0.056	0.12	0.11	-0.18	0.029	0.22	-0.22	0.015	nan	nan	0.011	0.16	0.3	0.33	-0.54	0.21	1	-0.83	0.00057	-0.011	-0.0021	-0.014	-0.0025	-0.0077	-0.02	-0.0082	-0.0066	-0.0086	0.024	-0.01	-0.018	-0.02	-0.012	-0.011	0.0038	-0.033	-0.048	0.01	-0.082	0.038	0.08	-0.047	-0.087	0.08	-0.055	-0.088	0.078	-0.06	-0.087	0.074	-0.057	-0.085	0.07	-0.033	-0.064	0.072	-0.038	-0.09	0.082	-0.054	-0.095	0.075	-0.056	-0.086	0.089	-0.058	-0.097	0.089	-0.056	-0.097
away_player_Y11	0.0076	-0.0086	-0.0086	-0.0037	0.14	-0.0063	-0.014	0.013	-0.04	nan	-0.011	-0.0027	-0.028	0.035	0.2	0.13	-0.25	-0.072	0.22	-0.21	nan	-0.024	0.023	-0.04	0.067	0.36	0.18	-0.62	-0.13	0.79	-0.81	nan	nan	0.007	0.01	-0.032	-0.2	-0.17	0.22	0.00032	-0.23	0.27	nan	nan	nan	0.007	-0.066	-0.4	-0.34	0.56	-0.13	-0.83	1	0.018	0.025	0.01	0.024	0.0085	0.016	0.028	0.021	0.014	0.022	-0.017	0.028	0.027	0.015	0.029	0.023	-0.00049	0.047	0.068	-0.0044	0.091	-0.034	-0.086	0.024	0.062	-0.084	0.035	0.067	-0.082	0.044	0.067	-0.078	0.039	0.065	-0.088	0.0097	0.054	-0.076	0.014	0.069	-0.083	0.031	0.067	-0.08	0.037	0.066	-0.085	0.036	0.071	-0.086	0.031	0.067
home_player_1	0.11	0.073	0.073	-0.0045	0.39	0.043	0.051	-0.016	0.028	0.0075	0.041	0.034	0.00051	-0.024	0.094	0.099	-0.026	-0.13	0.053	0.0054	0.0059	0.033	0.041	-0.0004	-0.021	0.09	0.089	-0.013	-0.13	0.029	0.033	0.006	0.0087	0.0075	-0.00016	0.021	-0.00048	-0.0099	0.083	0.12	-0.032	0.043	0.00073	nan	0.0074	0.0054	0.014	0.0028	0.0062	0.077	0.14	0.00057	0.018	1	0.3	0.25	0.27	0.27	0.26	0.27	0.27	0.26	0.29	0.27	0.17	0.23	0.2	0.21	0.22	0.22	0.23	0.24	0.25	0.23	0.23	0.056	-0.024	-0.074	0.061	-0.026	-0.074	0.063	-0.026	-0.073	0.069	-0.013	-0.061	0.065	-0.07	-0.1	0.058	-0.029	-0.072	0.032	-0.023	-0.062	0.065	-0.0092	-0.066	0.036	-0.015	-0.055	0.03	-0.015	-0.052
home_player_2	0.14	0.1	0.1	0.0076	0.49	0.1	0.077	-0.027	0.029	0.01	0.0099	0.024	-0.016	0.01	0.11	0.11	-0.032	-0.17	0.071	0.025	-0.0043	0.014	0.035	-0.03	0.016	0.093	0.095	-0.024	-0.16	0.042	0.044	0.0085	0.011	-0.0055	-0.01	-0.015	0.0081	-0.001	0.11	0.17	-0.039	0.045	-0.0043	nan	nan	0.0086	-0.022	0.014	0.016	0.11	0.16	-0.011	0.025	0.3	1	0.3	0.3	0.32	0.31	0.32	0.32	0.32	0.33	0.32	0.24	0.31	0.28	0.26	0.27	0.29	0.28	0.29	0.29	0.3	0.3	0.047	0.0031	-0.058	0.049	-0.0058	-0.057	0.052	-0.0019	-0.052	0.059	0.016	-0.038	0.026	-0.049	-0.071	0.049	-0.0058	-0.053	0.054	-0.0085	-0.068	0.053	0.02	-0.044	0.06	-0.0067	-0.065	0.06	-0.0032	-0.065
home_player_3	0.16	0.13	0.13	0.016	0.41	0.066	0.053	-0.0039	0.028	0.0089	0.0095	0.02	-0.05	0.036	0.093	0.089	-0.043	-0.15	0.069	0.023	0.013	0.016	0.028	-0.033	0.022	0.084	0.082	-0.017	-0.15	0.035	0.048	0.0081	0.0089	-0.004	0.0042	-0.051	-0.0013	-0.0023	0.1	0.14	-0.037	0.033	0.0067	nan	0.013	0.011	-0.031	0.015	0.018	0.086	0.15	-0.0021	0.01	0.25	0.3	1	0.26	0.29	0.29	0.29	0.28	0.3	0.31	0.31	0.22	0.27	0.25	0.24	0.26	0.27	0.24	0.27	0.28	0.26	0.26	0.039	0.019	-0.03	0.041	0.01	-0.032	0.041	0.013	-0.028	0.048	0.028	-0.013	0.027	-0.012	-0.031	0.038	0.017	-0.031	0.037	-0.0059	-0.052	0.046	0.039	-0.02	0.043	-0.0084	-0.049	0.039	-0.00082	-0.05
home_player_4	0.17	0.13	0.13	-0.0068	0.42	0.082	0.078	-0.011	0.025	0.0092	0.044	0.053	-0.026	-0.0037	0.096	0.1	-0.033	-0.15	0.062	0.017	-0.003	0.028	0.036	-0.023	0.0031	0.087	0.087	-0.027	-0.14	0.041	0.029	0.007	0.011	-0.0036	-0.0059	-0.0087	0.01	-0.00067	0.099	0.14	-0.035	0.041	-0.0057	nan	-0.0014	0.0015	-0.012	0.0087	0.0074	0.095	0.14	-0.014	0.024	0.27	0.3	0.26	1	0.3	0.29	0.27	0.29	0.31	0.32	0.31	0.21	0.25	0.23	0.24	0.23	0.26	0.24	0.27	0.27	0.27	0.26	0.051	0.008	-0.043	0.052	0.0042	-0.041	0.054	0.0044	-0.04	0.061	0.017	-0.025	0.045	-0.035	-0.053	0.053	0.0045	-0.042	0.048	-0.014	-0.057	0.058	0.025	-0.03	0.042	-0.019	-0.051	0.04	-0.018	-0.054
home_player_5	0.11	0.073	0.073	0.0084	0.46	0.055	0.053	-0.025	0.036	0.0087	0.049	0.052	-0.013	-0.02	0.093	0.096	-0.012	-0.16	0.06	0.034	-0.0063	0.019	0.041	-0.02	0.0039	0.088	0.079	-0.014	-0.14	0.034	0.046	0.0065	0.011	0.011	0.0081	0.006	0.018	0.016	0.087	0.15	-0.026	0.032	-0.0079	nan	-0.0047	-0.006	-0.013	0.021	0.031	0.093	0.15	-0.0025	0.0085	0.27	0.32	0.29	0.3	1	0.28	0.28	0.29	0.31	0.31	0.32	0.22	0.26	0.25	0.25	0.26	0.26	0.25	0.27	0.28	0.28	0.27	0.064	-0.0037	-0.066	0.065	-0.012	-0.066	0.068	-0.0087	-0.063	0.074	0.007	-0.046	0.055	-0.059	-0.085	0.066	-0.013	-0.063	0.051	-0.016	-0.067	0.07	0.013	-0.053	0.044	-0.0046	-0.051	0.042	-0.00015	-0.052
home_player_6	0.15	0.11	0.11	0.013	0.47	0.079	0.058	-0.01	0.037	0.009	0.015	0.036	-0.022	-0.00019	0.061	0.07	0.011	-0.13	0.03	0.051	-0.00093	0.031	0.046	-0.033	0.005	0.082	0.078	-0.0025	-0.15	0.037	0.045	0.007	0.011	0.0026	-0.0056	-0.0089	0.041	0.04	0.064	0.14	-0.0034	-0.0028	-0.0022	nan	-0.00026	0.0061	-0.013	0.018	0.024	0.078	0.15	-0.0077	0.016	0.26	0.31	0.29	0.29	0.28	1	0.28	0.3	0.32	0.31	0.3	0.23	0.29	0.26	0.26	0.26	0.27	0.27	0.28	0.28	0.27	0.28	0.057	0.0098	-0.046	0.059	0.00095	-0.045	0.062	0.0056	-0.047	0.07	0.022	-0.025	0.041	-0.037	-0.054	0.059	0.0049	-0.046	0.062	-0.01	-0.068	0.065	0.028	-0.034	0.072	-0.0018	-0.058	0.071	0.0071	-0.058
home_player_7	0.15	0.11	0.11	-0.0034	0.44	0.069	0.063	-0.0065	0.018	0.0087	0.015	0.028	-0.049	0.024	0.12	0.073	-0.046	-0.16	0.08	0.016	0.0049	0.019	0.032	-0.026	0.013	0.089	0.092	-0.028	-0.15	0.048	0.031	0.0072	0.0097	-0.0038	-0.0089	-0.035	-0.0061	0.027	0.11	0.15	-0.048	0.049	0.0049	nan	nan	-0.0031	-0.019	0.013	0.011	0.1	0.15	-0.02	0.028	0.27	0.32	0.29	0.27	0.28	0.28	1	0.28	0.29	0.3	0.28	0.21	0.29	0.26	0.25	0.26	0.27	0.25	0.26	0.27	0.28	0.28	0.057	0.028	-0.035	0.057	0.023	-0.033	0.058	0.02	-0.032	0.065	0.038	-0.015	0.045	-0.014	-0.047	0.057	0.024	-0.032	0.042	0.0088	-0.043	0.063	0.045	-0.023	0.047	0.023	-0.029	0.047	0.027	-0.03
home_player_8	0.13	0.091	0.091	0.0096	0.48	0.07	0.065	-0.017	0.022	0.012	0.054	0.053	-0.0032	-0.028	0.13	0.14	-0.068	-0.16	0.098	-0.019	0.0095	0.021	0.038	-0.016	-0.00019	0.11	0.086	-0.024	-0.15	0.039	0.034	0.011	0.011	0.0075	0.002	0.024	-0.0061	-0.026	0.14	0.15	-0.067	0.076	0.0061	nan	0.0097	0.0067	-0.0079	-1.2e-05	0.012	0.1	0.15	-0.0082	0.021	0.27	0.32	0.28	0.29	0.29	0.3	0.28	1	0.31	0.32	0.31	0.23	0.29	0.26	0.25	0.26	0.28	0.26	0.28	0.29	0.28	0.27	0.05	0.0091	-0.044	0.053	0.0047	-0.043	0.052	0.01	-0.043	0.06	0.022	-0.023	0.04	-0.029	-0.045	0.053	0.0047	-0.041	0.041	-0.0037	-0.058	0.057	0.028	-0.029	0.061	-0.011	-0.065	0.057	-0.0091	-0.063
home_player_9	0.12	0.076	0.076	-2.1e-05	0.49	0.095	0.058	-0.013	0.025	0.0086	0.029	0.04	-0.027	-0.0014	0.082	0.08	0.0051	-0.16	0.048	0.049	-0.0064	0.033	0.028	-0.017	0.0008	0.093	0.081	-0.012	-0.15	0.036	0.047	0.0064	0.01	0.0042	0.002	-0.0075	0.031	0.033	0.078	0.16	-0.015	0.016	-0.0077	nan	-0.005	-0.00075	-0.013	0.014	0.022	0.092	0.16	-0.0066	0.014	0.26	0.32	0.3	0.31	0.31	0.32	0.29	0.31	1	0.32	0.3	0.23	0.31	0.27	0.27	0.28	0.29	0.29	0.29	0.3	0.29	0.29	0.035	0.011	-0.038	0.036	0.0053	-0.037	0.04	0.011	-0.036	0.046	0.027	-0.018	0.019	-0.043	-0.054	0.036	0.0061	-0.034	0.031	0.0028	-0.041	0.042	0.031	-0.026	0.03	0.025	-0.019	0.033	0.028	-0.025
home_player_10	0.14	0.1	0.1	0.015	0.47	0.077	0.07	-0.034	0.026	0.0048	0.034	0.044	-0.026	-0.0051	0.13	0.13	-0.081	-0.16	0.12	-0.043	-0.0052	0.022	0.037	-0.031	0.0085	0.097	0.096	-0.027	-0.15	0.037	0.036	0.0037	0.0058	-0.0051	-0.00026	-0.0034	-0.024	-0.036	0.14	0.13	-0.096	0.1	-0.0052	nan	-0.0044	-0.0043	-0.018	0.0037	0.007	0.095	0.15	-0.0086	0.022	0.29	0.33	0.31	0.32	0.31	0.31	0.3	0.32	0.32	1	0.32	0.24	0.3	0.26	0.27	0.27	0.28	0.27	0.29	0.29	0.28	0.28	0.076	-0.025	-0.09	0.078	-0.032	-0.088	0.081	-0.031	-0.088	0.088	-0.015	-0.069	0.071	-0.073	-0.1	0.08	-0.035	-0.085	0.071	-0.043	-0.098	0.084	-0.0081	-0.077	0.063	-0.048	-0.096	0.063	-0.047	-0.097
home_player_11	0.11	0.074	0.074	-0.0039	0.44	0.076	0.054	-0.034	0.031	0.01	0.019	0.022	-0.024	0.0082	0.085	0.066	0.029	-0.19	0.035	0.097	0.0036	0.025	0.032	-0.033	0.0053	0.078	0.072	0.01	-0.15	0.011	0.073	0.01	0.0092	-0.004	5.3e-05	-0.014	0.044	0.053	0.058	0.2	0.017	-0.021	0.0025	nan	0.0036	0.0033	-0.013	0.033	0.038	0.073	0.16	0.024	-0.017	0.27	0.32	0.31	0.31	0.32	0.3	0.28	0.31	0.3	0.32	1	0.23	0.3	0.26	0.27	0.26	0.28	0.27	0.28	0.28	0.28	0.28	0.072	-0.012	-0.075	0.074	-0.023	-0.075	0.075	-0.024	-0.075	0.082	-0.0053	-0.058	0.07	-0.065	-0.095	0.071	-0.015	-0.075	0.054	-0.026	-0.078	0.08	0.0046	-0.061	0.056	-0.033	-0.078	0.054	-0.028	-0.078
away_player_1	0.1	0.07	0.07	0.0026	0.39	0.048	0.047	0.035	-0.013	0.0099	0.036	0.042	-0.01	-0.019	0.092	0.096	-0.022	-0.14	0.055	0.024	0.0088	0.039	0.043	-0.0099	-0.026	0.1	0.092	-0.021	-0.13	0.028	0.022	0.011	0.0078	-0.0036	-0.001	0.012	0.0076	0.0078	0.085	0.14	-0.021	0.024	0.012	nan	0.0064	0.0087	0.021	-0.011	-0.0026	0.081	0.12	-0.01	0.028	0.17	0.24	0.22	0.21	0.22	0.23	0.21	0.23	0.23	0.24	0.23	1	0.3	0.25	0.27	0.27	0.26	0.28	0.27	0.26	0.27	0.26	-0.068	0.047	0.051	-0.066	0.046	0.056	-0.065	0.048	0.054	-0.06	0.06	0.072	-0.11	0.022	0.067	-0.066	0.042	0.052	-0.054	0.034	0.027	-0.062	0.065	0.064	-0.056	0.034	0.036	-0.052	0.036	0.03
away_player_2	0.15	0.1	0.1	0.012	0.49	0.062	0.095	0.028	-0.0067	0.0075	0.03	0.043	-0.031	0.0098	0.097	0.097	-0.031	-0.17	0.076	0.023	0.0015	0.021	0.026	-0.035	0.0091	0.11	0.1	-0.023	-0.17	0.054	0.04	0.0057	0.0091	-0.0046	0.00018	-0.018	0.021	0.011	0.11	0.16	-0.039	0.043	-0.0028	nan	0.0032	0.0011	-0.0097	0.0099	0.013	0.1	0.18	-0.018	0.027	0.23	0.31	0.27	0.25	0.26	0.29	0.29	0.29	0.31	0.3	0.3	0.3	1	0.3	0.31	0.33	0.31	0.32	0.32	0.32	0.34	0.34	-0.033	0.062	0.037	-0.032	0.053	0.039	-0.028	0.06	0.041	-0.023	0.076	0.062	-0.057	0.019	0.037	-0.031	0.054	0.04	-0.037	0.042	0.023	-0.025	0.081	0.051	-0.05	0.057	0.05	-0.051	0.061	0.042
away_player_3	0.16	0.13	0.13	0.012	0.41	0.049	0.072	0.027	-0.007	0.009	0.029	0.028	-0.021	0.0088	0.094	0.083	-0.021	-0.16	0.059	0.036	0.0044	1.6e-05	0.0089	-0.047	0.035	0.097	0.079	-0.028	-0.16	0.049	0.041	0.0072	0.01	-0.004	-0.0089	-0.019	0.019	0.025	0.089	0.16	-0.021	0.028	-0.00056	nan	0.006	0.0039	-0.049	-0.0061	0.0094	0.09	0.15	-0.02	0.015	0.2	0.28	0.25	0.23	0.25	0.26	0.26	0.26	0.27	0.26	0.26	0.25	0.3	1	0.26	0.29	0.3	0.28	0.28	0.3	0.3	0.31	-0.02	0.054	0.031	-0.019	0.049	0.034	-0.016	0.047	0.033	-0.012	0.062	0.049	-0.028	0.014	0.016	-0.019	0.052	0.031	-0.044	0.051	0.043	-0.015	0.074	0.041	-0.053	0.068	0.068	-0.054	0.073	0.059
away_player_4	0.16	0.13	0.13	0.0017	0.43	0.069	0.083	0.017	-0.0065	0.0085	0.025	0.023	-0.015	0.0063	0.079	0.08	-0.022	-0.13	0.053	0.029	0.0027	0.034	0.049	-0.021	-0.006	0.093	0.097	-0.025	-0.14	0.036	0.023	0.0066	0.01	-2.9e-05	9.4e-05	-0.016	0.019	0.013	0.084	0.13	-0.021	0.017	-0.0017	nan	0.0042	0.0062	-0.0016	0.0027	-0.0019	0.088	0.14	-0.012	0.029	0.21	0.26	0.24	0.24	0.25	0.26	0.25	0.25	0.27	0.27	0.27	0.27	0.31	0.26	1	0.29	0.28	0.28	0.3	0.31	0.32	0.31	-0.017	0.057	0.024	-0.017	0.051	0.031	-0.015	0.048	0.028	-0.0072	0.066	0.044	-0.034	0.022	0.019	-0.014	0.049	0.031	-0.036	0.039	0.029	-0.0095	0.075	0.04	-0.038	0.047	0.044	-0.038	0.047	0.035
away_player_5	0.11	0.068	0.068	0.011	0.46	0.058	0.059	0.023	-0.011	0.012	0.03	0.04	-0.022	-0.0031	0.089	0.084	-0.022	-0.14	0.058	0.028	0.0017	0.038	0.047	-0.0057	-0.012	0.1	0.089	-0.017	-0.16	0.05	0.041	0.0098	0.013	-0.0037	0.0036	-0.0084	0.022	0.024	0.097	0.15	-0.021	0.024	-0.0021	nan	0.003	0.0076	0.0031	0.0097	0.014	0.097	0.17	-0.011	0.023	0.22	0.27	0.26	0.23	0.26	0.26	0.26	0.26	0.28	0.27	0.26	0.27	0.33	0.29	0.29	1	0.28	0.28	0.29	0.31	0.32	0.32	-0.037	0.049	0.031	-0.037	0.041	0.034	-0.036	0.043	0.037	-0.029	0.058	0.051	-0.062	-0.0081	0.022	-0.034	0.041	0.037	-0.041	0.041	0.027	-0.03	0.066	0.045	-0.031	0.059	0.042	-0.031	0.062	0.036
away_player_6	0.14	0.11	0.11	0.013	0.47	0.055	0.067	0.016	0.0064	0.011	0.028	0.039	-0.016	-0.004	0.082	0.088	-0.021	-0.14	0.06	0.029	-0.0032	0.03	0.038	-0.017	-0.012	0.063	0.076	0.0077	-0.13	0.024	0.05	0.0088	0.013	-0.0038	-0.0053	-0.0048	0.023	0.021	0.094	0.14	-0.027	0.029	-0.0019	nan	-0.0033	-0.00019	0.0037	0.04	0.03	0.066	0.14	0.0038	-0.00049	0.22	0.29	0.27	0.26	0.26	0.27	0.27	0.28	0.29	0.28	0.28	0.26	0.31	0.3	0.28	0.28	1	0.29	0.3	0.31	0.31	0.3	-0.018	0.046	0.021	-0.017	0.037	0.025	-0.018	0.043	0.025	-0.0054	0.059	0.041	-0.035	-0.0018	0.012	-0.017	0.042	0.025	-0.045	0.037	0.027	-0.011	0.065	0.034	-0.061	0.061	0.064	-0.057	0.068	0.059
away_player_7	0.14	0.11	0.11	0.0064	0.44	0.054	0.078	0.025	0.0047	0.0084	0.024	0.039	-0.028	0.0087	0.089	0.091	-0.029	-0.15	0.068	0.026	-0.00099	0.0033	0.023	-0.037	0.021	0.13	0.068	-0.04	-0.16	0.069	0.017	0.0066	0.0098	-0.0036	-0.0021	-0.017	0.014	0.013	0.098	0.15	-0.034	0.036	-0.002	nan	-0.00042	0.0011	-0.028	-0.017	0.032	0.11	0.15	-0.033	0.047	0.23	0.28	0.24	0.24	0.25	0.27	0.25	0.26	0.29	0.27	0.27	0.28	0.32	0.28	0.28	0.28	0.29	1	0.29	0.28	0.31	0.29	-0.017	0.066	0.035	-0.016	0.059	0.038	-0.014	0.061	0.039	-0.0076	0.076	0.058	-0.037	0.029	0.037	-0.016	0.06	0.037	-0.027	0.046	0.025	-0.0096	0.082	0.046	-0.027	0.052	0.034	-0.024	0.058	0.031
away_player_8	0.12	0.085	0.085	0.017	0.48	0.045	0.067	0.011	-0.00041	0.011	0.028	0.03	-0.021	0.0033	0.1	0.092	-0.038	-0.15	0.066	0.023	0.0012	0.043	0.053	-0.004	-0.026	0.12	0.13	-0.059	-0.16	0.076	-0.0057	0.011	0.0099	-0.0049	-0.0024	-0.012	0.0054	0.0069	0.11	0.15	-0.033	0.032	0.0051	nan	-0.00063	-0.0024	0.02	-0.012	-0.024	0.13	0.15	-0.048	0.068	0.24	0.29	0.27	0.27	0.27	0.28	0.26	0.28	0.29	0.29	0.28	0.27	0.32	0.28	0.3	0.29	0.3	0.29	1	0.32	0.32	0.32	-0.029	0.056	0.035	-0.028	0.051	0.039	-0.029	0.056	0.039	-0.017	0.069	0.056	-0.045	0.016	0.033	-0.027	0.051	0.037	-0.051	0.045	0.031	-0.021	0.074	0.048	-0.05	0.045	0.039	-0.047	0.046	0.032
away_player_9	0.12	0.076	0.076	0.0048	0.49	0.052	0.087	0.013	-0.0039	0.015	0.025	0.047	-0.032	0.0063	0.094	0.095	-0.024	-0.16	0.06	0.039	0.0044	0.035	0.041	-0.017	-0.012	0.094	0.083	0.011	-0.16	0.019	0.067	0.015	0.013	-0.0041	-0.00033	-0.02	0.012	0.0091	0.094	0.16	-0.023	0.022	0.0048	nan	0.0036	0.005	0.00096	0.024	0.036	0.074	0.17	0.01	-0.0044	0.25	0.29	0.28	0.27	0.28	0.28	0.27	0.29	0.3	0.29	0.28	0.26	0.32	0.3	0.31	0.31	0.31	0.28	0.32	1	0.33	0.32	-0.033	0.054	0.03	-0.031	0.046	0.032	-0.029	0.054	0.034	-0.023	0.068	0.054	-0.065	0.0029	0.029	-0.031	0.048	0.034	-0.026	0.055	0.026	-0.026	0.074	0.045	-0.015	0.054	0.027	-0.016	0.059	0.024
away_player_10	0.14	0.1	0.1	0.011	0.47	0.063	0.083	0.026	-0.022	0.013	0.036	0.031	-0.041	0.014	0.093	0.094	-0.033	-0.16	0.065	0.03	-0.00076	0.028	0.035	-0.028	-0.0017	0.13	0.12	-0.082	-0.15	0.1	-0.038	0.012	0.012	-0.0046	-0.00036	-0.025	0.017	0.015	0.099	0.15	-0.032	0.032	-0.00076	nan	nan	0.0035	-0.0045	-0.024	-0.03	0.14	0.13	-0.082	0.091	0.23	0.3	0.26	0.27	0.28	0.27	0.28	0.28	0.29	0.28	0.28	0.27	0.34	0.3	0.32	0.32	0.31	0.31	0.32	0.33	1	0.31	-0.071	0.054	0.053	-0.07	0.047	0.057	-0.069	0.049	0.056	-0.062	0.066	0.076	-0.1	0.018	0.058	-0.069	0.048	0.057	-0.08	0.046	0.052	-0.065	0.077	0.069	-0.09	0.056	0.069	-0.088	0.06	0.064
away_player_11	0.11	0.077	0.077	0.0049	0.45	0.06	0.09	0.024	-0.0074	0.0077	0.03	0.035	-0.023	-0.0068	0.081	0.074	0.014	-0.16	0.034	0.067	0.0098	0.019	0.024	-0.015	-0.0052	0.087	0.073	0.04	-0.18	0.01	0.11	0.0058	0.0092	-0.00058	-0.00092	-0.0043	0.033	0.037	0.066	0.16	0.0039	-0.0036	0.0021	nan	0.012	0.0085	-0.00035	0.037	0.055	0.054	0.2	0.038	-0.034	0.23	0.3	0.26	0.26	0.27	0.28	0.28	0.27	0.29	0.28	0.28	0.26	0.34	0.31	0.31	0.32	0.3	0.29	0.32	0.32	0.31	1	-0.058	0.047	0.041	-0.057	0.04	0.046	-0.056	0.043	0.045	-0.05	0.055	0.061	-0.083	0.00098	0.038	-0.057	0.044	0.041	-0.065	0.04	0.041	-0.051	0.064	0.053	-0.068	0.056	0.066	-0.067	0.06	0.055
B365H	0.064	0.06	0.06	0.0072	0.046	0.045	-0.032	-0.26	0.3	-0.0039	-0.021	-0.012	-0.016	0.00076	0.018	-0.03	-0.027	0.027	0.04	-0.051	-0.0021	0.026	0.013	0.001	0.012	-0.021	0.028	0.061	-0.074	-0.056	0.11	-0.0043	-0.003	-0.0022	-0.0072	-0.01	-0.018	0.016	0.017	-0.039	-0.056	0.039	-0.0041	nan	-0.00095	-0.00023	-0.011	0.057	0.031	-0.022	0.095	0.08	-0.086	0.056	0.047	0.039	0.051	0.064	0.057	0.057	0.05	0.035	0.076	0.072	-0.068	-0.033	-0.02	-0.017	-0.037	-0.018	-0.017	-0.029	-0.033	-0.071	-0.058	1	0.018	-0.47	0.99	0.021	-0.48	0.98	0.0053	-0.5	0.99	0.015	-0.47	0.99	0.033	-0.43	0.99	0.012	-0.46	0.99	-0.022	-0.49	0.99	0.017	-0.44	0.99	-0.026	-0.52	0.99	-0.033	-0.51
B365D	0.1	0.093	0.093	0.031	0.11	-0.022	0.018	0.27	-0.074	0.0057	0.0046	-0.009	-0.028	0.028	0.013	0.029	0.035	-0.1	-0.0032	0.091	-0.0046	-0.02	-0.024	-0.044	0.018	0.039	-0.011	-0.02	-0.034	0.051	-0.0086	0.0045	0.0065	-0.0027	-0.00058	-0.024	0.047	0.042	0.025	0.12	0.035	-0.047	-0.0049	nan	-0.0038	-0.0057	-0.025	-0.0084	0.032	0.042	0.024	-0.047	0.024	-0.024	0.0031	0.019	0.008	-0.0037	0.0098	0.028	0.0091	0.011	-0.025	-0.012	0.047	0.062	0.054	0.057	0.049	0.046	0.066	0.056	0.054	0.054	0.047	0.018	1	0.82	0.0016	0.97	0.81	-0.012	0.96	0.81	0.012	0.97	0.82	0.072	0.98	0.83	0.014	0.96	0.83	-0.016	0.97	0.83	0.039	0.98	0.84	-0.038	0.98	0.82	-0.033	0.98	0.83
B365A	0.036	0.034	0.034	0.0098	0.0084	-0.036	0.039	0.36	-0.23	0.0057	0.019	0.002	-0.0065	0.011	-0.0066	0.024	0.057	-0.084	-0.035	0.1	-0.003	-0.026	-0.025	-0.03	-0.0066	0.032	-0.043	-0.037	0.03	0.072	-0.073	0.0053	0.0058	-0.0019	0.0042	-0.0034	0.039	0.024	-0.017	0.1	0.064	-0.07	-0.0021	nan	-0.003	-0.0044	-0.00099	-0.042	0.0089	0.02	-0.052	-0.087	0.062	-0.074	-0.058	-0.03	-0.043	-0.066	-0.046	-0.035	-0.044	-0.038	-0.09	-0.075	0.051	0.037	0.031	0.024	0.031	0.021	0.035	0.035	0.03	0.053	0.041	-0.47	0.82	1	-0.49	0.82	0.97	-0.5	0.82	0.97	-0.48	0.81	0.97	-0.43	0.83	0.98	-0.48	0.81	0.97	-0.49	0.82	0.98	-0.45	0.81	0.97	-0.52	0.82	0.98	-0.51	0.83	0.98
BWH	0.061	0.057	0.057	0.007	0.051	0.046	-0.033	-0.26	0.3	-0.0031	-0.02	-0.012	-0.017	-0.0027	0.027	-0.027	-0.032	0.024	0.046	-0.054	-0.0018	0.026	0.014	0.0046	0.0096	-0.016	0.034	0.058	-0.078	-0.055	0.11	-0.0037	-0.0022	-0.0017	-0.0071	-0.0073	-0.025	0.012	0.022	-0.038	-0.06	0.044	-0.004	nan	-0.00067	-7.3e-05	-0.0077	0.053	0.025	-0.02	0.099	0.08	-0.084	0.061	0.049	0.041	0.052	0.065	0.059	0.057	0.053	0.036	0.078	0.074	-0.066	-0.032	-0.019	-0.017	-0.037	-0.017	-0.016	-0.028	-0.031	-0.07	-0.057	0.99	0.0016	-0.49	1	0.0033	-0.5	0.98	-0.011	-0.52	0.98	-0.00041	-0.48	0.98	0.018	-0.45	0.98	-0.0042	-0.48	0.99	-0.035	-0.5	0.98	0.0013	-0.45	0.99	-0.039	-0.54	0.99	-0.048	-0.52
BWD	0.11	0.096	0.096	0.04	0.099	-0.02	0.018	0.27	-0.072	0.0046	0.0078	-0.0092	-0.024	0.021	0.025	0.035	0.026	-0.1	0.0029	0.082	-0.0047	-0.018	-0.024	-0.043	0.011	0.053	-0.0056	-0.03	-0.034	0.057	-0.018	0.0038	0.0052	-0.0023	0.00042	-0.016	0.033	0.03	0.028	0.11	0.027	-0.038	-0.0043	nan	-0.0042	-0.0057	-0.018	-0.024	0.021	0.049	0.022	-0.055	0.035	-0.026	-0.0058	0.01	0.0042	-0.012	0.00095	0.023	0.0047	0.0053	-0.032	-0.023	0.046	0.053	0.049	0.051	0.041	0.037	0.059	0.051	0.046	0.047	0.04	0.021	0.97	0.82	0.0033	1	0.81	-0.01	0.96	0.8	0.014	0.96	0.82	0.073	0.97	0.83	0.016	0.96	0.82	-0.01	0.97	0.82	0.04	0.97	0.84	-0.032	0.98	0.81	-0.027	0.97	0.82
BWA	0.041	0.038	0.038	0.012	0.014	-0.036	0.041	0.35	-0.23	0.0065	0.025	0.0063	-0.0018	0.0027	-0.00053	0.033	0.055	-0.087	-0.034	0.1	-0.0029	-0.021	-0.022	-0.031	-0.0098	0.041	-0.033	-0.042	0.025	0.073	-0.076	0.0061	0.0064	-0.0018	0.006	0.0056	0.035	0.016	-0.015	0.1	0.063	-0.066	-0.0018	nan	-0.003	-0.0043	0.0025	-0.05	0.00055	0.026	-0.048	-0.088	0.067	-0.074	-0.057	-0.032	-0.041	-0.066	-0.045	-0.033	-0.043	-0.037	-0.088	-0.075	0.056	0.039	0.034	0.031	0.034	0.025	0.038	0.039	0.032	0.057	0.046	-0.48	0.81	0.97	-0.5	0.81	1	-0.51	0.81	0.97	-0.49	0.8	0.96	-0.44	0.81	0.98	-0.49	0.8	0.97	-0.51	0.8	0.97	-0.47	0.8	0.97	-0.54	0.81	0.98	-0.53	0.81	0.97
IWH	0.063	0.058	0.058	0.012	0.057	0.041	-0.033	-0.27	0.3	-0.0031	-0.019	-0.0097	-0.013	-0.0025	0.024	-0.025	-0.031	0.023	0.046	-0.054	-0.0022	0.028	0.014	0.0052	0.011	-0.016	0.037	0.057	-0.081	-0.053	0.11	-0.0036	-0.0023	-0.0022	-0.0076	-0.0076	-0.02	0.014	0.022	-0.036	-0.06	0.045	-0.0038	nan	-0.0013	-0.00045	-0.0089	0.053	0.025	-0.016	0.1	0.078	-0.082	0.063	0.052	0.041	0.054	0.068	0.062	0.058	0.052	0.04	0.081	0.075	-0.065	-0.028	-0.016	-0.015	-0.036	-0.018	-0.014	-0.029	-0.029	-0.069	-0.056	0.98	-0.012	-0.5	0.98	-0.01	-0.51	1	-0.024	-0.53	0.98	-0.013	-0.5	0.97	0.0047	-0.46	0.98	-0.018	-0.49	0.98	-0.049	-0.51	0.97	-0.013	-0.46	0.99	-0.057	-0.55	0.98	-0.065	-0.54
IWD	0.096	0.082	0.082	0.05	0.12	-0.018	0.023	0.27	-0.083	0.0054	0.013	-0.0012	-0.02	0.013	0.029	0.044	0.023	-0.1	0.0047	0.074	-0.005	-0.014	-0.019	-0.036	0.0026	0.055	-0.001	-0.03	-0.034	0.06	-0.025	0.0043	0.0061	-0.0026	0.0011	-0.0074	0.027	0.018	0.028	0.11	0.024	-0.03	-0.005	nan	-0.0043	-0.0057	-0.01	-0.026	0.015	0.046	0.02	-0.06	0.044	-0.026	-0.0019	0.013	0.0044	-0.0087	0.0056	0.02	0.01	0.011	-0.031	-0.024	0.048	0.06	0.047	0.048	0.043	0.043	0.061	0.056	0.054	0.049	0.043	0.0053	0.96	0.82	-0.011	0.96	0.81	-0.024	1	0.83	-0.00081	0.95	0.82	0.066	0.95	0.83	0.0022	0.95	0.83	-0.029	0.96	0.82	0.026	0.95	0.84	-0.059	0.96	0.82	-0.055	0.96	0.83
IWA	0.041	0.039	0.039	0.018	0.012	-0.038	0.036	0.36	-0.23	0.0071	0.02	0.0022	-0.0064	0.01	-2.4e-05	0.033	0.053	-0.092	-0.031	0.1	-0.0031	-0.024	-0.023	-0.026	-0.0084	0.038	-0.037	-0.038	0.022	0.073	-0.072	0.0063	0.0075	-0.002	0.0043	-0.0017	0.033	0.014	-0.015	0.11	0.063	-0.066	-0.0029	nan	-0.0027	-0.0042	0.00078	-0.045	0.0062	0.022	-0.043	-0.087	0.067	-0.073	-0.052	-0.028	-0.04	-0.063	-0.047	-0.032	-0.043	-0.036	-0.088	-0.075	0.054	0.041	0.033	0.028	0.037	0.025	0.039	0.039	0.034	0.056	0.045	-0.5	0.81	0.97	-0.52	0.8	0.97	-0.53	0.83	1	-0.51	0.79	0.96	-0.46	0.81	0.97	-0.51	0.8	0.96	-0.52	0.8	0.96	-0.48	0.79	0.95	-0.55	0.81	0.97	-0.54	0.81	0.97
LBH	0.07	0.064	0.064	0.0073	0.072	0.048	-0.031	-0.26	0.29	-0.0023	-0.019	-0.01	-0.016	-0.002	0.022	-0.029	-0.03	0.025	0.045	-0.053	-0.0012	0.031	0.016	0.00015	0.01	-0.017	0.034	0.053	-0.074	-0.051	0.1	-0.0028	-0.0015	-0.002	-0.0071	-0.008	-0.017	0.018	0.023	-0.037	-0.06	0.043	-0.0031	nan	-0.00017	0.0005	-0.0089	0.057	0.03	-0.012	0.095	0.074	-0.078	0.069	0.059	0.048	0.061	0.074	0.07	0.065	0.06	0.046	0.088	0.082	-0.06	-0.023	-0.012	-0.0072	-0.029	-0.0054	-0.0076	-0.017	-0.023	-0.062	-0.05	0.99	0.012	-0.48	0.98	0.014	-0.49	0.98	-0.00081	-0.51	1	0.013	-0.48	0.99	0.024	-0.44	0.98	0.0049	-0.47	0.98	-0.034	-0.5	0.98	0.012	-0.44	0.99	-0.041	-0.54	0.99	-0.046	-0.52
LBD	0.12	0.1	0.1	0.038	0.15	-0.022	0.016	0.26	-0.072	0.0065	0.012	-0.0021	-0.025	0.019	0.023	0.037	0.025	-0.11	0.0098	0.077	-0.0062	-0.014	-0.021	-0.045	0.011	0.051	-0.0079	-0.03	-0.033	0.059	-0.022	0.005	0.0077	-0.0021	-0.00054	-0.015	0.045	0.037	0.038	0.12	0.02	-0.03	-0.0062	nan	-0.0054	-0.0067	-0.019	-0.016	0.027	0.052	0.02	-0.057	0.039	-0.013	0.016	0.028	0.017	0.007	0.022	0.038	0.022	0.027	-0.015	-0.0053	0.06	0.076	0.062	0.066	0.058	0.059	0.076	0.069	0.068	0.066	0.055	0.015	0.97	0.81	-0.00041	0.96	0.8	-0.013	0.95	0.79	0.013	1	0.82	0.069	0.98	0.82	0.013	0.95	0.82	-0.021	0.96	0.81	0.037	0.97	0.83	-0.046	0.96	0.8	-0.04	0.96	0.81
LBA	0.045	0.038	0.038	0.014	0.054	-0.034	0.046	0.35	-0.23	0.0069	0.027	0.012	-0.0049	0.0055	-7.2e-05	0.031	0.052	-0.089	-0.026	0.096	-0.0039	-0.023	-0.022	-0.034	-0.0071	0.04	-0.04	-0.037	0.023	0.072	-0.071	0.0059	0.0076	-0.0018	0.0045	0.0016	0.041	0.023	-0.0069	0.1	0.056	-0.06	-0.0035	nan	-0.0035	-0.0048	-0.00084	-0.044	0.01	0.023	-0.044	-0.085	0.065	-0.061	-0.038	-0.013	-0.025	-0.046	-0.025	-0.015	-0.023	-0.018	-0.069	-0.058	0.072	0.062	0.049	0.044	0.051	0.041	0.058	0.056	0.054	0.076	0.061	-0.47	0.82	0.97	-0.48	0.82	0.96	-0.5	0.82	0.96	-0.48	0.82	1	-0.42	0.83	0.98	-0.47	0.81	0.96	-0.51	0.81	0.96	-0.45	0.82	0.96	-0.54	0.81	0.97	-0.53	0.81	0.98
PSH	0.079	0.079	0.079	0.0092	0.027	0.052	-0.04	-0.25	0.3	-0.0047	-0.026	-0.014	-0.018	0.0035	0.038	-0.038	-0.022	0.028	0.03	-0.037	-0.0047	0.037	0.014	0.0029	0.004	-0.049	0.0078	0.063	-0.061	-0.047	0.097	-0.0047	nan	nan	-0.0083	-0.014	-0.051	0.013	0.00087	-0.038	-0.047	0.034	-0.0047	nan	nan	0.0014	-0.0061	0.078	0.044	-0.038	0.078	0.07	-0.088	0.065	0.026	0.027	0.045	0.055	0.041	0.045	0.04	0.019	0.071	0.07	-0.11	-0.057	-0.028	-0.034	-0.062	-0.035	-0.037	-0.045	-0.065	-0.1	-0.083	0.99	0.072	-0.43	0.98	0.073	-0.44	0.97	0.066	-0.46	0.99	0.069	-0.42	1	0.054	-0.41	0.99	0.067	-0.42	0.99	0.023	-0.45	0.99	0.065	-0.4	0.99	0.033	-0.5	0.99	0.0056	-0.49
PSD	0.12	0.12	0.12	0.031	0.028	-0.031	0.013	0.27	-0.074	-0.0022	0.011	-0.016	-0.026	0.025	0.008	0.0052	0.043	-0.11	0.0057	0.088	-0.0022	-0.029	-0.034	-0.049	0.03	0.05	-0.023	-0.02	-0.021	0.036	-0.0021	-0.0022	nan	nan	0.0041	-0.023	0.039	0.049	-0.002	0.11	0.031	-0.052	-0.0022	nan	nan	-0.0063	-0.037	-0.048	0.016	0.016	0.008	-0.033	0.0097	-0.07	-0.049	-0.012	-0.035	-0.059	-0.037	-0.014	-0.029	-0.043	-0.073	-0.065	0.022	0.019	0.014	0.022	-0.0081	-0.0018	0.029	0.016	0.0029	0.018	0.00098	0.033	0.98	0.83	0.018	0.97	0.81	0.0047	0.95	0.81	0.024	0.98	0.83	0.054	1	0.83	0.025	0.97	0.84	-0.013	0.98	0.85	0.049	0.99	0.85	0.0018	0.97	0.8	-0.016	0.98	0.82
PSA	0.052	0.049	0.049	0.0038	0.014	-0.044	0.051	0.35	-0.23	0.0015	0.032	0.01	0.0041	-0.0093	-0.00014	0.024	0.041	-0.081	-0.014	0.078	0.0015	-0.033	-0.027	-0.031	-0.0033	0.075	-0.03	-0.044	0.031	0.05	-0.06	0.0015	nan	nan	0.014	0.014	0.044	0.03	0.003	0.093	0.045	-0.055	0.0015	nan	nan	-0.005	-0.005	-0.089	-0.0064	0.027	-0.05	-0.064	0.054	-0.1	-0.071	-0.031	-0.053	-0.085	-0.054	-0.047	-0.045	-0.054	-0.1	-0.095	0.067	0.037	0.016	0.019	0.022	0.012	0.037	0.033	0.029	0.058	0.038	-0.43	0.83	0.98	-0.45	0.83	0.98	-0.46	0.83	0.97	-0.44	0.82	0.98	-0.41	0.83	1	-0.44	0.82	0.98	-0.46	0.84	0.98	-0.42	0.83	0.98	-0.49	0.82	0.98	-0.5	0.84	0.98
WHH	0.066	0.06	0.06	0.013	0.063	0.041	-0.032	-0.26	0.29	-0.0027	-0.018	-0.011	-0.016	-0.0033	0.025	-0.028	-0.034	0.029	0.047	-0.059	-0.0014	0.031	0.015	0.003	0.0079	-0.015	0.034	0.053	-0.075	-0.049	0.1	-0.0033	-0.0019	-0.0015	-0.0067	-0.0061	-0.023	0.013	0.023	-0.043	-0.064	0.048	-0.0033	nan	-0.00034	0.00022	-0.006	0.053	0.026	-0.013	0.093	0.072	-0.076	0.058	0.049	0.038	0.053	0.066	0.059	0.057	0.053	0.036	0.08	0.071	-0.066	-0.031	-0.019	-0.014	-0.034	-0.017	-0.016	-0.027	-0.031	-0.069	-0.057	0.99	0.014	-0.48	0.98	0.016	-0.49	0.98	0.0022	-0.51	0.98	0.013	-0.47	0.99	0.025	-0.44	1	0.0038	-0.47	0.99	-0.025	-0.49	0.99	0.013	-0.44	0.99	-0.032	-0.53	0.99	-0.039	-0.51
WHD	0.11	0.1	0.1	0.032	0.076	-0.021	0.019	0.27	-0.072	0.0033	-0.0013	-0.012	-0.028	0.029	0.012	0.024	0.043	-0.1	-0.012	0.1	-0.0053	-0.025	-0.03	-0.046	0.021	0.039	-0.016	-0.013	-0.034	0.043	0.002	0.0025	0.004	-0.0029	-0.00029	-0.024	0.046	0.044	0.016	0.12	0.046	-0.058	-0.0053	nan	-0.0047	-0.0056	-0.027	-0.012	0.032	0.033	0.026	-0.038	0.014	-0.029	-0.0058	0.017	0.0045	-0.013	0.0049	0.024	0.0047	0.0061	-0.035	-0.015	0.042	0.054	0.052	0.049	0.041	0.042	0.06	0.051	0.048	0.048	0.044	0.012	0.96	0.81	-0.0042	0.96	0.8	-0.018	0.95	0.8	0.0049	0.95	0.81	0.067	0.97	0.82	0.0038	1	0.82	-0.017	0.96	0.82	0.032	0.96	0.83	-0.04	0.96	0.8	-0.034	0.97	0.81
WHA	0.044	0.039	0.039	0.016	0.029	-0.035	0.033	0.35	-0.22	0.0075	0.022	0.0049	-0.0021	0.0057	0.003	0.032	0.049	-0.088	-0.026	0.095	-0.0033	-0.026	-0.026	-0.03	-0.0071	0.041	-0.038	-0.045	0.029	0.075	-0.078	0.007	0.0076	-0.0022	0.0045	0.0022	0.035	0.019	-0.0062	0.1	0.054	-0.058	-0.0023	nan	-0.0033	-0.0046	-0.00099	-0.05	0.0046	0.028	-0.052	-0.09	0.069	-0.072	-0.053	-0.031	-0.042	-0.063	-0.046	-0.032	-0.041	-0.034	-0.085	-0.075	0.052	0.04	0.031	0.031	0.037	0.025	0.037	0.037	0.034	0.057	0.041	-0.46	0.83	0.97	-0.48	0.82	0.97	-0.49	0.83	0.96	-0.47	0.82	0.96	-0.42	0.84	0.98	-0.47	0.82	1	-0.49	0.83	0.97	-0.44	0.82	0.97	-0.52	0.82	0.97	-0.51	0.83	0.98
SJH	0.056	0.054	0.054	0.005	0.029	0.024	-0.017	-0.25	0.29	-0.0085	-0.02	-0.013	-0.016	0.00054	-7.8e-05	-0.033	-0.019	0.029	0.041	-0.049	-0.002	0.0051	0.0019	0.0041	0.021	-0.018	0.03	0.059	-0.069	-0.059	0.11	-0.0086	-0.0076	-0.0024	-0.0082	-0.0086	-0.0017	0.02	0.014	-0.039	-0.052	0.029	-0.0043	nan	-0.00079	0.00018	-0.016	0.044	0.018	-0.023	0.091	0.082	-0.083	0.032	0.054	0.037	0.048	0.051	0.062	0.042	0.041	0.031	0.071	0.054	-0.054	-0.037	-0.044	-0.036	-0.041	-0.045	-0.027	-0.051	-0.026	-0.08	-0.065	0.99	-0.016	-0.49	0.99	-0.01	-0.51	0.98	-0.029	-0.52	0.98	-0.021	-0.51	0.99	-0.013	-0.46	0.99	-0.017	-0.49	1	-0.02	-0.49	0.99	-0.016	-0.46	0.99	-0.025	-0.52	0.99	-0.032	-0.51
SJD	0.098	0.09	0.09	0.031	0.088	-0.015	0.0075	0.27	-0.076	0.0035	-0.024	-0.025	-0.025	0.042	0.0069	0.04	0.021	-0.09	-0.0092	0.086	-0.0058	-0.036	-0.03	-0.032	0.024	0.029	0.0018	-0.03	-0.035	0.056	-0.02	0.0027	0.0043	-0.002	1.3e-05	-0.034	0.039	0.016	0.026	0.1	0.038	-0.047	-0.0057	nan	-0.0051	-0.0072	-0.028	-0.0033	0.016	0.051	0.025	-0.054	0.031	-0.023	-0.0085	-0.0059	-0.014	-0.016	-0.01	0.0088	-0.0037	0.0028	-0.043	-0.026	0.034	0.042	0.051	0.039	0.041	0.037	0.046	0.045	0.055	0.046	0.04	-0.022	0.97	0.82	-0.035	0.97	0.8	-0.049	0.96	0.8	-0.034	0.96	0.81	0.023	0.98	0.84	-0.025	0.96	0.83	-0.02	1	0.83	-0.0045	0.96	0.84	-0.044	0.97	0.81	-0.04	0.97	0.82
SJA	0.033	0.032	0.032	0.012	-0.0076	-0.02	0.016	0.35	-0.22	0.0068	-0.00043	-0.0072	-0.0039	0.019	-0.0039	0.032	0.049	-0.081	-0.035	0.1	-0.0035	-0.022	-0.021	-0.018	-0.009	0.022	-0.033	-0.038	0.021	0.081	-0.079	0.0063	0.0068	-0.0024	0.0049	-0.0097	0.029	0.0047	-0.02	0.096	0.062	-0.065	-0.0026	nan	-0.0035	-0.0052	0.0027	-0.028	0.0056	0.024	-0.044	-0.095	0.067	-0.062	-0.068	-0.052	-0.057	-0.067	-0.068	-0.043	-0.058	-0.041	-0.098	-0.078	0.027	0.023	0.043	0.029	0.027	0.027	0.025	0.031	0.026	0.052	0.041	-0.49	0.83	0.98	-0.5	0.82	0.97	-0.51	0.82	0.96	-0.5	0.81	0.96	-0.45	0.85	0.98	-0.49	0.82	0.97	-0.49	0.83	1	-0.47	0.82	0.97	-0.51	0.83	0.98	-0.5	0.83	0.98
VCH	0.075	0.069	0.069	0.0097	0.068	0.047	-0.03	-0.25	0.29	-0.0013	-0.016	-0.0097	-0.016	-0.0032	0.028	-0.023	-0.032	0.019	0.046	-0.052	-0.0017	0.03	0.016	0.0032	0.0064	-0.013	0.034	0.057	-0.079	-0.05	0.11	-0.002	-0.00033	-0.0018	-0.0066	-0.0066	-0.022	0.012	0.026	-0.032	-0.06	0.045	-0.0036	nan	-0.00061	0.00011	-0.0052	0.054	0.029	-0.015	0.1	0.075	-0.08	0.065	0.053	0.046	0.058	0.07	0.065	0.063	0.057	0.042	0.084	0.08	-0.062	-0.025	-0.015	-0.0095	-0.03	-0.011	-0.0096	-0.021	-0.026	-0.065	-0.051	0.99	0.039	-0.45	0.98	0.04	-0.47	0.97	0.026	-0.48	0.98	0.037	-0.45	0.99	0.049	-0.42	0.99	0.032	-0.44	0.99	-0.0045	-0.47	1	0.038	-0.42	0.98	-0.006	-0.51	0.98	-0.013	-0.49
VCD	0.12	0.11	0.11	0.036	0.15	-0.019	0.019	0.27	-0.07	0.0059	0.0095	-0.0074	-0.029	0.023	0.032	0.043	0.027	-0.12	0.012	0.088	-0.004	-0.02	-0.023	-0.048	0.018	0.06	0.0044	-0.032	-0.052	0.062	-0.012	0.0051	0.0064	-0.0027	-0.00068	-0.02	0.038	0.035	0.037	0.13	0.023	-0.034	-0.0038	nan	-0.0035	-0.0048	-0.025	-0.022	0.02	0.057	0.04	-0.056	0.037	-0.0092	0.02	0.039	0.025	0.013	0.028	0.045	0.028	0.031	-0.0081	0.0046	0.065	0.081	0.074	0.075	0.066	0.065	0.082	0.074	0.074	0.077	0.064	0.017	0.98	0.81	0.0013	0.97	0.8	-0.013	0.95	0.79	0.012	0.97	0.82	0.065	0.99	0.83	0.013	0.96	0.82	-0.016	0.96	0.82	0.038	1	0.84	-0.037	0.97	0.8	-0.032	0.97	0.81
VCA	0.056	0.05	0.05	0.017	0.045	-0.033	0.037	0.34	-0.22	0.0073	0.027	0.0052	-0.0034	0.0032	0.0033	0.033	0.056	-0.095	-0.027	0.1	-0.0028	-0.022	-0.02	-0.034	-0.0071	0.047	-0.029	-0.042	0.015	0.074	-0.07	0.0068	0.0074	-0.0017	0.0061	0.0038	0.038	0.023	-0.0097	0.11	0.058	-0.064	-0.0018	nan	-0.0029	-0.0042	-0.00061	-0.05	0.0026	0.031	-0.037	-0.086	0.066	-0.066	-0.044	-0.02	-0.03	-0.053	-0.034	-0.023	-0.029	-0.026	-0.077	-0.061	0.064	0.051	0.041	0.04	0.045	0.034	0.046	0.048	0.045	0.069	0.053	-0.44	0.84	0.97	-0.45	0.84	0.97	-0.46	0.84	0.95	-0.44	0.83	0.96	-0.4	0.85	0.98	-0.44	0.83	0.97	-0.46	0.84	0.97	-0.42	0.84	1	-0.49	0.84	0.97	-0.49	0.84	0.97
GBH	0.051	0.05	0.05	0.0059	0.025	0.018	-0.012	-0.26	0.3	-0.0056	-0.019	-0.014	-0.014	-0.00022	0.0017	-0.029	-0.025	0.031	0.041	-0.057	-0.0021	0.004	0.0038	0.0047	0.02	-0.0083	0.041	0.056	-0.074	-0.065	0.12	-0.0062	-0.0045	-0.0023	-0.0046	-0.0065	-0.00091	0.017	0.021	-0.043	-0.054	0.038	-0.0048	nan	-0.00059	-0.00059	-0.013	0.035	0.011	-0.021	0.099	0.089	-0.085	0.036	0.06	0.043	0.042	0.044	0.072	0.047	0.061	0.03	0.063	0.056	-0.056	-0.05	-0.053	-0.038	-0.031	-0.061	-0.027	-0.05	-0.015	-0.09	-0.068	0.99	-0.038	-0.52	0.99	-0.032	-0.54	0.99	-0.059	-0.55	0.99	-0.046	-0.54	0.99	0.0018	-0.49	0.99	-0.04	-0.52	0.99	-0.044	-0.51	0.98	-0.037	-0.49	1	-0.042	-0.54	0.99	-0.049	-0.52
GBD	0.095	0.088	0.088	0.033	0.11	-0.0087	0.0087	0.27	-0.069	0.0038	-0.0099	-0.012	-0.02	0.029	0.0044	0.049	0.018	-0.075	-0.019	0.078	-0.006	-0.031	-0.032	-0.031	0.013	0.027	-0.0039	-0.027	-0.028	0.058	-0.027	0.0032	0.0043	-0.0039	-0.0068	-0.02	0.04	0.01	0.03	0.088	0.039	-0.042	-0.0057	nan	-0.0054	-0.0054	-0.015	0.0029	0.023	0.054	0.018	-0.058	0.036	-0.015	-0.0067	-0.0084	-0.019	-0.0046	-0.0018	0.023	-0.011	0.025	-0.048	-0.033	0.034	0.057	0.068	0.047	0.059	0.061	0.052	0.045	0.054	0.056	0.056	-0.026	0.98	0.82	-0.039	0.98	0.81	-0.057	0.96	0.81	-0.041	0.96	0.81	0.033	0.97	0.82	-0.032	0.96	0.82	-0.025	0.97	0.83	-0.006	0.97	0.84	-0.042	1	0.82	-0.038	0.98	0.83
GBA	0.025	0.023	0.023	0.012	0.016	-0.011	0.026	0.35	-0.23	0.0084	0.01	0.00092	-0.0013	0.012	-0.0024	0.041	0.051	-0.076	-0.045	0.1	-0.004	-0.017	-0.024	-0.019	-0.015	0.018	-0.038	-0.032	0.022	0.082	-0.087	0.0079	0.0082	-0.0027	-0.0036	-0.003	0.022	-0.0061	-0.027	0.092	0.068	-0.064	-0.0023	nan	-0.0041	-0.0041	0.01	-0.023	0.0097	0.019	-0.047	-0.097	0.071	-0.055	-0.065	-0.049	-0.051	-0.051	-0.058	-0.029	-0.065	-0.019	-0.096	-0.078	0.036	0.05	0.068	0.044	0.042	0.064	0.034	0.039	0.027	0.069	0.066	-0.52	0.82	0.98	-0.54	0.81	0.98	-0.55	0.82	0.97	-0.54	0.8	0.97	-0.5	0.8	0.98	-0.53	0.8	0.97	-0.52	0.81	0.98	-0.51	0.8	0.97	-0.54	0.82	1	-0.53	0.82	0.98
BSH	0.049	0.048	0.048	0.0095	0.027	0.018	-0.012	-0.25	0.3	-0.0064	-0.019	-0.015	-0.014	0.00082	-0.0042	-0.034	-0.021	0.036	0.037	-0.055	-0.002	0.0032	0.0034	0.0037	0.02	-0.011	0.038	0.058	-0.071	-0.066	0.12	-0.0068	-0.0054	-0.0026	-0.0046	-0.007	0.0039	0.021	0.018	-0.047	-0.051	0.034	-0.0045	nan	-0.0007	-0.0007	-0.014	0.038	0.014	-0.021	0.096	0.089	-0.086	0.03	0.06	0.039	0.04	0.042	0.071	0.047	0.057	0.033	0.063	0.054	-0.052	-0.051	-0.054	-0.038	-0.031	-0.057	-0.024	-0.047	-0.016	-0.088	-0.067	0.99	-0.033	-0.51	0.99	-0.027	-0.53	0.98	-0.055	-0.54	0.99	-0.04	-0.53	0.99	-0.016	-0.5	0.99	-0.034	-0.51	0.99	-0.04	-0.5	0.98	-0.032	-0.49	0.99	-0.038	-0.53	1	-0.044	-0.52
BSD	0.096	0.089	0.089	0.034	0.11	-0.0067	0.011	0.27	-0.073	0.0049	-0.012	-0.014	-0.022	0.031	-0.00098	0.043	0.026	-0.076	-0.023	0.086	-0.0061	-0.031	-0.029	-0.03	0.014	0.022	-0.008	-0.022	-0.026	0.056	-0.023	0.004	0.0056	-0.0026	-0.0056	-0.023	0.046	0.017	0.025	0.091	0.045	-0.048	-0.0056	nan	-0.0055	-0.0055	-0.017	0.0063	0.026	0.048	0.018	-0.056	0.031	-0.015	-0.0032	-0.00082	-0.018	-0.00015	0.0071	0.027	-0.0091	0.028	-0.047	-0.028	0.036	0.061	0.073	0.047	0.062	0.068	0.058	0.046	0.059	0.06	0.06	-0.033	0.98	0.83	-0.048	0.97	0.81	-0.065	0.96	0.81	-0.046	0.96	0.81	0.0056	0.98	0.84	-0.039	0.97	0.83	-0.032	0.97	0.83	-0.013	0.97	0.84	-0.049	0.98	0.82	-0.044	1	0.83
BSA	0.024	0.022	0.022	0.013	0.013	-0.011	0.021	0.35	-0.22	0.0088	0.0076	-0.00077	-0.0042	0.016	-0.014	0.032	0.057	-0.07	-0.049	0.11	-0.0041	-0.017	-0.023	-0.02	-0.011	0.00056	-0.054	-0.023	0.031	0.082	-0.084	0.0081	0.0089	-0.0024	-0.0039	-0.0065	0.033	0.0037	-0.031	0.087	0.071	-0.069	-0.0029	nan	-0.0041	-0.0041	0.0063	-0.009	0.023	0.011	-0.053	-0.097	0.067	-0.052	-0.065	-0.05	-0.054	-0.052	-0.058	-0.03	-0.063	-0.025	-0.097	-0.078	0.03	0.042	0.059	0.035	0.036	0.059	0.031	0.032	0.024	0.064	0.055	-0.51	0.83	0.98	-0.52	0.82	0.97	-0.54	0.83	0.97	-0.52	0.81	0.98	-0.49	0.82	0.98	-0.51	0.81	0.98	-0.51	0.82	0.98	-0.49	0.81	0.97	-0.52	0.83	0.98	-0.52	0.83	1

	count	mean	std	min	25%	50%	75%	max
id	11060.0	5537.511392	3197.692647	1.00	2767.75	5536.50	8306.25	11075.00
player_api_id	11060.0	156582.427215	160713.700624	2625.00	35555.50	96619.50	212470.50	750584.00
player_fifa_api_id	11060.0	165664.910488	58649.928360	2.00	151889.50	184671.00	203883.25	234141.00
height	11060.0	181.867445	6.369201	157.48	177.80	182.88	185.42	208.28
weight	11060.0	168.380289	14.990217	117.00	159.00	168.00	179.00	243.00

	id	player_api_id	player_fifa_api_id	height	weight
id	1	-0.0065	-0.0024	0.0086	0.0047
player_api_id	-0.0065	1	0.58	-0.054	-0.16
player_fifa_api_id	-0.0024	0.58	1	-0.026	-0.11
height	0.0086	-0.054	-0.026	1	0.77
weight	0.0047	-0.16	-0.11	0.77	1

	count	mean	std	min	25%	50%	75%	max
id	183978.0	91989.500000	53110.018250	1.0	45995.25	91989.5	137983.75	183978.0
player_fifa_api_id	183978.0	165671.524291	53851.094769	2.0	155798.00	183488.0	199848.00	234141.0
player_api_id	183978.0	135900.617324	136927.840510	2625.0	34763.00	77741.0	191080.00	750584.0
overall_rating	183142.0	68.600015	7.041139	33.0	64.00	69.0	73.00	94.0
potential	183142.0	73.460353	6.592271	39.0	69.00	74.0	78.00	97.0
crossing	183142.0	55.086883	17.242135	1.0	45.00	59.0	68.00	95.0
finishing	183142.0	49.921078	19.038705	1.0	34.00	53.0	65.00	97.0
heading_accuracy	183142.0	57.266023	16.488905	1.0	49.00	60.0	68.00	98.0
short_passing	183142.0	62.429672	14.194068	3.0	57.00	65.0	72.00	97.0
volleys	181265.0	49.468436	18.256618	1.0	35.00	52.0	64.00	93.0
dribbling	183142.0	59.175154	17.744688	1.0	52.00	64.0	72.00	97.0
curve	181265.0	52.965675	18.255788	2.0	41.00	56.0	67.00	94.0
free_kick_accuracy	183142.0	49.380950	17.831746	1.0	36.00	50.0	63.00	97.0
long_passing	183142.0	57.069880	14.394464	3.0	49.00	59.0	67.00	97.0
ball_control	183142.0	63.388879	15.196671	5.0	58.00	67.0	73.00	97.0
acceleration	183142.0	67.659357	12.983326	10.0	61.00	69.0	77.00	97.0
sprint_speed	183142.0	68.051244	12.569721	12.0	62.00	69.0	77.00	97.0
agility	181265.0	65.970910	12.954585	11.0	58.00	68.0	75.00	96.0
reactions	183142.0	66.103706	9.155408	17.0	61.00	67.0	72.00	96.0
balance	181265.0	65.189496	13.063188	12.0	58.00	67.0	74.00	96.0
shot_power	183142.0	61.808427	16.135143	2.0	54.00	65.0	73.00	97.0
jumping	181265.0	66.969045	11.006734	14.0	60.00	68.0	74.00	96.0
stamina	183142.0	67.038544	13.165262	10.0	61.00	69.0	76.00	96.0
strength	183142.0	67.424529	12.072280	10.0	60.00	69.0	76.00	96.0
long_shots	183142.0	53.339431	18.367025	1.0	41.00	58.0	67.00	96.0
aggression	183142.0	60.948046	16.089521	6.0	51.00	64.0	73.00	97.0
interceptions	183142.0	52.009271	19.450133	1.0	34.00	57.0	68.00	96.0
positioning	183142.0	55.786504	18.448292	2.0	45.00	60.0	69.00	96.0
vision	181265.0	57.873550	15.144086	1.0	49.00	60.0	69.00	97.0
penalties	183142.0	55.003986	15.546519	2.0	45.00	57.0	67.00	96.0
marking	183142.0	46.772242	21.227667	1.0	25.00	50.0	66.00	96.0
standing_tackle	183142.0	50.351257	21.483706	1.0	29.00	56.0	69.00	95.0
sliding_tackle	181265.0	48.001462	21.598778	2.0	25.00	53.0	67.00	95.0
gk_diving	183142.0	14.704393	16.865467	1.0	7.00	10.0	13.00	94.0
gk_handling	183142.0	16.063612	15.867382	1.0	8.00	11.0	15.00	93.0
gk_kicking	183142.0	20.998362	21.452980	1.0	8.00	12.0	15.00	97.0
gk_positioning	183142.0	16.132154	16.099175	1.0	8.00	11.0	15.00	96.0
gk_reflexes	183142.0	16.441439	17.198155	1.0	8.00	11.0	15.00	96.0

	count	mean	std	min	25%	50%	75%	max
id	299.0	23735.301003	15167.914719	1.0	9552.50	22805.0	36250.50	51606.0
team_api_id	299.0	12340.521739	25940.411135	1601.0	8349.00	8655.0	9886.50	274581.0
team_fifa_api_id	288.0	21534.305556	42456.439408	1.0	178.75	673.5	1910.75	112513.0

	count	mean	std	min	25%	50%	75%	max
id	1458.0	729.500000	421.032659	1.0	365.25	729.5	1093.75	1458.0
team_fifa_api_id	1458.0	17706.982167	39179.857739	1.0	110.00	485.0	1900.00	112513.0
team_api_id	1458.0	9995.727023	13264.869900	1601.0	8457.75	8674.0	9904.00	274581.0
buildUpPlaySpeed	1458.0	52.462277	11.545869	20.0	45.00	52.0	62.00	80.0
buildUpPlayDribbling	489.0	48.607362	9.678290	24.0	42.00	49.0	55.00	77.0
buildUpPlayPassing	1458.0	48.490398	10.896101	20.0	40.00	50.0	55.00	80.0
chanceCreationPassing	1458.0	52.165295	10.360793	21.0	46.00	52.0	59.00	80.0
chanceCreationCrossing	1458.0	53.731824	11.086796	20.0	47.00	53.0	62.00	80.0
chanceCreationShooting	1458.0	53.969136	10.327566	22.0	48.00	53.0	61.00	80.0
defencePressure	1458.0	46.017147	10.227225	23.0	39.00	45.0	51.00	72.0
defenceAggression	1458.0	49.251029	9.738028	24.0	44.00	48.0	55.00	72.0
defenceTeamWidth	1458.0	52.185871	9.574712	29.0	47.00	52.0	58.00	73.0

	id	country_id	league_id	season	stage	date	match_api_id	home_team_api_id	away_team_api_id	home_team_goal	...	SJA	VCH	VCD	VCA	GBH	GBD	GBA	BSH	BSD	BSA
0	1	1	1	2008/2009	1	2008-08-17 00:00:00	492473	9987	9993	1	...	4.0	1.65	3.40	4.50	1.78	3.25	4.00	1.73	3.40	4.20
1	2	1	1	2008/2009	1	2008-08-16 00:00:00	492474	10000	9994	0	...	3.8	2.00	3.25	3.25	1.85	3.25	3.75	1.91	3.25	3.60
2	3	1	1	2008/2009	1	2008-08-16 00:00:00	492475	9984	8635	0	...	2.5	2.35	3.25	2.65	2.50	3.20	2.50	2.30	3.20	2.75

	id	player_fifa_api_id	player_api_id	date	overall_rating	potential	preferred_foot	attacking_work_rate	defensive_work_rate	crossing	...	vision	penalties	marking	standing_tackle	sliding_tackle	gk_diving	gk_handling	gk_kicking	gk_positioning	gk_reflexes
0	1	218353	505942	2016-02-18 00:00:00	67.0	71.0	right	medium	medium	49.0	...	54.0	48.0	65.0	69.0	69.0	6.0	11.0	10.0	8.0	8.0
1	2	218353	505942	2015-11-19 00:00:00	67.0	71.0	right	medium	medium	49.0	...	54.0	48.0	65.0	69.0	69.0	6.0	11.0	10.0	8.0	8.0
2	3	218353	505942	2015-09-21 00:00:00	62.0	66.0	right	medium	medium	49.0	...	54.0	48.0	65.0	66.0	69.0	6.0	11.0	10.0	8.0	8.0

	id	team_api_id	team_fifa_api_id	team_long_name	team_short_name
0	1	9987	673.0	KRC Genk	GEN
1	2	9993	675.0	Beerschot AC	BAC
2	3	10000	15005.0	SV Zulte-Waregem	ZUL

	id	team_api_id	team_fifa_api_id	team_long_name	team_short_name
8	9	7947	NaN	FCV Dender EH	DEN
14	15	4049	NaN	Tubize	TUB
170	26561	6601	NaN	FC Volendam	VOL
204	34816	177361	NaN	Termalica Bruk-Bet Nieciecza	TBN
208	35286	7992	NaN	Trofense	TRO
213	35291	10213	NaN	Amadora	AMA
223	36248	9765	NaN	Portimonense	POR
225	36723	4064	NaN	Feirense	FEI
232	38789	6367	NaN	Uniao da Madeira	MAD
233	38791	188163	NaN	Tondela	TON
298	51606	7896	NaN	Lugano	LUG

	id	team_fifa_api_id	team_api_id	date	buildUpPlaySpeed	buildUpPlaySpeedClass	buildUpPlayDribbling	buildUpPlayDribblingClass	buildUpPlayPassing	buildUpPlayPassingClass	...	chanceCreationShooting	chanceCreationShootingClass	chanceCreationPositioningClass	defencePressure	defencePressureClass	defenceAggression	defenceAggressionClass	defenceTeamWidth	defenceTeamWidthClass	defenceDefenderLineClass
0	1	434	9930	2010-02-22 00:00:00	60	Balanced	NaN	Little	50	Mixed	...	55	Normal	Organised	50	Medium	55	Press	45	Normal	Cover
1	2	434	9930	2014-09-19 00:00:00	52	Balanced	48.0	Normal	56	Mixed	...	64	Normal	Organised	47	Medium	44	Press	54	Normal	Cover
2	3	434	9930	2015-09-10 00:00:00	47	Balanced	41.0	Normal	54	Mixed	...	64	Normal	Organised	47	Medium	44	Press	54	Normal	Cover

	id	player_api_id	player_name	player_fifa_api_id	birthday	height	weight
0	1	505942	Aaron Appindangoye	218353	1992-02-29 00:00:00	182.88	187
1	2	155782	Aaron Cresswell	189615	1989-12-15 00:00:00	170.18	146
2	3	162549	Aaron Doran	186170	1991-05-13 00:00:00	170.18	163

	player_api_id	player_fifa_api_id	birthday	height	weight	age	date	overall_rating	potential	preferred_foot	...	vision	penalties	marking	standing_tackle	sliding_tackle	gk_diving	gk_handling	gk_kicking	gk_positioning	gk_reflexes
player_name
Aaron Appindangoye	5	5	5	5	5	5	5	5	5	5	...	5	5	5	5	5	5	5	5	5	5
Aaron Cresswell	33	33	33	33	33	33	33	33	33	33	...	33	33	33	33	33	33	33	33	33	33
Aaron Doran	26	26	26	26	26	26	26	26	26	26	...	26	26	26	26	26	26	26	26	26	26
Aaron Galindo	23	23	23	23	23	23	23	23	23	23	...	23	23	23	23	23	23	23	23	23	23
Aaron Hughes	25	25	25	25	25	25	25	25	25	25	...	25	25	25	25	25	25	25	25	25	25
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Zsolt Low	7	7	7	7	7	7	7	7	7	7	...	7	7	7	7	7	7	7	7	7	7
Zurab Khizanishvili	8	8	8	8	8	8	8	8	8	8	...	8	8	8	8	8	8	8	8	8	8
Zvjezdan Misimovic	10	10	10	10	10	10	10	10	10	10	...	10	10	10	10	10	10	10	10	10	10
de Oliveira Cleber Monteiro	9	9	9	9	9	9	9	9	9	9	...	9	9	9	9	9	9	9	9	9	9
dos Santos Fabio Junior	4	4	4	4	4	4	4	4	4	4	...	4	4	4	4	4	4	4	4	4	4

	player_api_id	player_name	player_fifa_api_id	birthday	height	weight	age	date	overall_rating	potential	...	vision	penalties	marking	standing_tackle	sliding_tackle	gk_diving	gk_handling	gk_kicking	gk_positioning	gk_reflexes
95620	307224	Kevin Koubemba	208127	1993-03-23	193.04	198	26	2016-07-07 00:00:00	64.0	68.0	...	55.0	65.0	22.0	22.0	25.0	12.0	12.0	7.0	11.0	12.0
57229	184521	Florian Lejeune	197948	1991-05-20	187.96	179	28	2016-07-07 00:00:00	73.0	77.0	...	32.0	43.0	74.0	75.0	69.0	11.0	15.0	15.0	12.0	7.0
181048	512726	Yanis Mbombo Lokwa	221274	1994-04-08	177.80	172	25	2016-07-07 00:00:00	63.0	72.0	...	48.0	59.0	15.0	16.0	12.0	11.0	12.0	12.0	12.0	7.0
178639	450002	Wallace	216437	1993-10-14	190.50	183	26	2016-07-07 00:00:00	74.0	82.0	...	31.0	45.0	76.0	78.0	74.0	15.0	11.0	11.0	10.0	11.0
153508	45400	Ronnie Schwartz	172555	1989-08-29	182.88	176	30	2016-07-07 00:00:00	68.0	70.0	...	45.0	66.0	23.0	17.0	23.0	13.0	11.0	6.0	9.0	14.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
115250	150119	Marko Vejinovic	189715	1990-02-03	185.42	152	29	2007-02-22 00:00:00	59.0	67.0	...	71.0	56.0	54.0	57.0	41.0	7.0	22.0	64.0	22.0	22.0
115219	12473	Marko Suler	186689	1983-03-09	185.42	174	36	2007-02-22 00:00:00	64.0	69.0	...	53.0	65.0	64.0	65.0	79.0	4.0	21.0	53.0	21.0	21.0
115203	213487	Marko Scepovic	220121	1991-05-23	190.50	183	28	2007-02-22 00:00:00	69.0	77.0	...	64.0	57.0	30.0	31.0	38.0	7.0	12.0	6.0	7.0	13.0
115190	425988	Marko Poletanovic	227150	1993-07-20	187.96	159	26	2007-02-22 00:00:00	66.0	74.0	...	67.0	54.0	53.0	62.0	61.0	14.0	13.0	14.0	10.0	12.0
183977	39902	Zvjezdan Misimovic	102359	1982-06-05	180.34	176	37	2007-02-22 00:00:00	80.0	81.0	...	88.0	53.0	38.0	32.0	30.0	9.0	9.0	78.0	7.0	15.0

	country	country_id	league
0	Belgium	1	Belgium Jupiler League
1	England	1729	England Premier League
2	France	4769	France Ligue 1
3	Germany	7809	Germany 1. Bundesliga
4	Italy	10257	Italy Serie A
5	Netherlands	13274	Netherlands Eredivisie
6	Poland	15722	Poland Ekstraklasa
7	Portugal	17642	Portugal Liga ZON Sagres
8	Scotland	19694	Scotland Premier League
9	Spain	21518	Spain LIGA BBVA
10	Switzerland	24558	Switzerland Super League

	home_team_win
home_team_name
FC Barcelona	0.861842
Real Madrid CF	0.848684
SL Benfica	0.822581
FC Porto	0.822581
FC Bayern Munich	0.801471
...	...
DSC Arminia Bielefeld	0.117647
AC Arles-Avignon	0.105263
Dunfermline Athletic	0.052632
Córdoba CF	0.052632
SpVgg Greuther Fürth	0.000000

	away_team_win
away_team_name
Rangers	0.684211
FC Barcelona	0.677632
SL Benfica	0.669355
FC Porto	0.653226
Real Madrid CF	0.651316
...	...
CD Tenerife	0.052632
AC Arles-Avignon	0.052632
CD Numancia	0.052632
Brescia	0.052632
FC Metz	0.052632

	birthday	date	overall_rating	age
95620	1993-03-23	2016-07-07	64.0	23
57229	1991-05-20	2016-07-07	73.0	25
181048	1994-04-08	2016-07-07	63.0	22
178639	1993-10-14	2016-07-07	74.0	23
153508	1989-08-29	2016-07-07	68.0	27

	birthday	date	overall_rating
age
17	9	9	9
18	40	40	40
19	164	164	164
20	336	336	336
21	437	437	437
22	591	591	591
23	598	598	598
24	709	709	709
25	748	748	748
26	752	752	752
27	771	771	771
28	829	829	829
29	786	786	786
30	696	696	696
31	640	640	640
32	625	625	625
33	545	545	545
34	483	483	483
35	431	431	431
36	291	291	291
37	165	165	165
38	103	103	103
39	36	36	36
40	28	28	28
41	22	22	22
42	7	7	7
43	4	4	4
44	2	2	2

	birthday	date	overall_rating
age
17	9	9	9
18	40	40	40
19	164	164	164
20	336	336	336
21	437	437	437
22	591	591	591
23	598	598	598
24	709	709	709
25	748	748	748
26	752	752	752
27	771	771	771
28	829	829	829
29	786	786	786
30	696	696	696
31	640	640	640
32	625	625	625
33	545	545	545
34	483	483	483
35	431	431	431
36	291	291	291
37	165	165	165
38	103	103	103
39	36	36	36
40	28	28	28
41	22	22	22
42	7	7	7
43	4	4	4
44	2	2	2